Bridging Design and Economics: A PSI Framework Analysis of Residency Matching Market Evolution
Bibliographic record
Abstract
This paper addresses a divide: economics and design remain largely disconnected despite shared concerns about shaping products, services, and social systems. We propose that bridging design theory and practices—especially the Problem-Social-Institutional (PSI) framework—into market economics can aid the design of complex products and institutions. We illustrate this potential through an in-depth analysis of the evolution of US, Canadian, and British medical residency matching markets, which assign medical graduates to hospitals. Initially considered a purely optimal allocation problem, these markets repeatedly failed, stemming mainly from information asymmetry and shifting participant needs. With PSI, we show how changes in problem framing, stakeholder roles, and institutional structures can realign these markets toward stability and better outcomes. This transdisciplinary view positions market design as an iterative, evolving process, much like engineering a product or service. Our conclusions suggest that economists can benefit from design theories such as PSI and design practices such as prototyping, simulation, and stakeholder engagement. Further, we contend that design theorists stand to deepen their practice by incorporating economic considerations that are largely ignored. PSI is positioned as a bridge between design and economics to serve as a common language and framework. • Design theory and practice offer a fresh perspective on market design and can contribute to design economics and the improved design of future markets. • Design researchers should study economics as practiced and valued and not as theorized to improve the grounding of their research in practice. • Designers should study economics to improve their capability to articulate the value they bring to economics beyond cost and price. • The collaboration of design and economics offers the potential of bootstrapping both disciplines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".