A novel, game-based talent assessment I built from scratch and deployed firmwide at a leading global hedge fund to evaluate current staff, guide hiring and improve investment performance.
CogCards is an online, game-based cognitive assessment that measures risk appetite and other key cognitive dimensions to guide hiring decisions and improve investment team performance.
Instead of a simple personality test, candidates play a complex, card-based gambling game, resulting in a metric-rich snapshot of their decision-making under stress and uncertainty.
Development involved constructing and validating a novel measurement framework that triangulated behavioral and survey-based data to predict real-world investment performance.
Stakeholders at a leading global hedge fund agreed that current risk-taking among the investment team was suboptimal — but had no reliable way to assess risk appetite and other key cognitive dimensions in current or prospective hires. It's well known in finance that optimal risk-taking is critical to investing skill.
Build a tool to assess risk appetite and decision-making ability for current and prospective employees that's engaging, valid, and able to be deployed at scale.
Can we identify behavioral patterns that impact investment performance — and what methodology, validated rigorously, can deliver that quickly with limited engineering resources?
Competitive analysis found no real precedent — just personality tests or small scientific instruments. Our objectives pointed toward combining a behavioral task with a brief self-report survey.
Built and piloted a game prototype and results dashboard, cycling through stakeholder feedback and analysis between rounds.
Shipped the final platform (JavaScript + Claude Code) with a custom-built Python-based interactive results dashboard, live with candidates and current analysts.
Validated the measurement framework internally and externally, then mined behavioral insights from the results.
A card-based gambling game where players try to win as much money as possible. Decision-making patterns as the game unfolds reveal risk appetite and other cognitive abilities.
Closed- and open-ended questions to triangulate against behavioral data and surface additional qualitative insights.
A polished cloud-deployed game platform, admin panel, and a dashboard surfacing player results for hiring teams.
Validating the framework internally (behavioral × self-report) and externally (against portfolio metrics) surfaced several surprising patterns: the current team's distribution of risk-taking profiles was missing a key range entirely, risk-taking was often suboptimal, the worst performers overestimated their own performance (and the best performers underestimated theirs), and there were surprisingly high rates of cognitive biases affecting decision-making.
Risk-taking was often suboptimal for maximizing gains in-game.
An unexpectedly high rate of cognitive biases, including gambler's fallacy.
Clear evidence of a Dunning-Kruger effect — overconfidence concentrated among weaker performers.
Open-ended responses (analyzed with Claude) surfaced recurring player archetypes.
Across: how they actually performed, as a percentile. Up: where they believed they had placed.
A player on the dashed line judged their own performance exactly. Above it, they overestimated; below it, they underestimated.
Players in the bottom third placed themselves far above where they actually finished.
Players in the top third placed themselves below their real standing. It's the classic Dunning–Kruger pattern, and overconfidence is a widespread and consequential problem in investing.
Some findings have been generalized or omitted, and all visuals are mockups, to preserve confidentiality. They remain faithful to the original results and content.