A Primer · Prepared for the Complexity Economics Program · September 2026

The OFR Dossier

The Office of Financial Research: the United States’ experiment in seeing its own financial system — how it was conceived, who built it, and why it sits unused.

2010

Created · Dodd-Frank Title I

196 → 70

Staff, 2024 → planned 2026

4+ yrs

No confirmed director

Subpoenas ever issued

I · What It Is

The OFR is a bureau inside the US Treasury, created by Dodd-Frank Act §§151–156 in July 2010, with two statutory jobs: give the Financial Stability Oversight Council the data to see the whole financial system, and the research to understand it. It holds powers no other financial agency has — the authority to standardize financial data across every regulator, and a subpoena power to compel data from any financial company. It is funded not by Congress but by assessments on large banks, which was meant to insulate it from politics and instead made it politically friendless.

It is also the closest any government has come to institutionalizing complexity economics: an agency whose founding premise was that the financial system is a network of contracts and obligations that no equilibrium model can summarize, and that must therefore be mapped, measured, and simulated.

II · The Events That Created It

SEP 2008
The weekend nobody could see

As Lehman Brothers failed, neither the Fed, the Treasury, nor the SEC could answer the operative question: who is exposed to whom, and for how much? Counterparty webs across derivatives, repo, and securities lending were invisible — not secret, just never collected in any common format. The bailout decisions of that autumn were made substantially blind.
2009
The CE-NIF — a National Institute of Finance

Allan Mendelowitz and John Liechty convene the Committee to Establish the National Institute of Finance: a volunteer campaign of economists, statisticians, and quants (with public support from Harry Markowitz, among others) proposing a standalone agency — an NIH for finance — with a data center and a research arm. Mendelowitz described the intended role as “like a biblical prophet — speaking truth to power.”
JUL 2010
Dodd-Frank compromise: an office, not an institute

Senator Jack Reed carries the proposal into Dodd-Frank. It survives — but as an office inside Treasury rather than an independent institute, with a Senate-confirmed director. Both compromises will matter: the first makes it subordinate to each administration’s Treasury, the second makes it decapitable by simple inaction.
2013–2017
The buildout under Richard Berner

First confirmed director Richard Berner builds the research corps and delivers the OFR’s most durable win: global adoption of the Legal Entity Identifier — a 20-character code (e.g. 5493…) giving every counterparty on earth a unique name, the addressing system a financial-network map requires. The working-paper series becomes a genuine home for network contagion, agent-based, and stress-testing research.
2017–2019
First dismantling

The first Trump Treasury cuts staff by roughly a third and shrinks the budget; the director departs; the mission is narrowed to “support” functions. The lesson: an agency created to ask uncomfortable questions has no natural constituency when nothing is on fire.
2020–2024
Vindication without restoration

March 2020’s Treasury-market seizure, Archegos in 2021, and the regional-bank runs of 2023 were each, precisely, failures to see leverage and exposure concentrations — the OFR’s founding problem. A partial rebuild follows: a hedge-fund monitor, and in 2024 the office’s first major mandatory data collection, covering non-centrally-cleared bilateral repo — the dark corner where the basis trade lives. No permanent director is confirmed after February 2022.
2025–2026
Second dismantling

Staff falls from 196 to roughly 100 during 2025; the FY2026 plan cuts to ~70 — a 60%+ reduction — with the $110M budget cut by nearly a quarter. The office runs under an acting director. Legislation to protect it (Rep. Bill Foster, a physicist) is introduced with little prospect. The smoke detector is being unplugged while private credit, stablecoins, and the Treasury basis trade grow in exactly its blind spots.

OFR full-time staff. The office is funded by bank assessments, not appropriations — the cuts are policy, not budget arithmetic.

III · Why It Is Not Leveraged Today

1. It was born compromised

The independent institute became an office inside Treasury — so its ambition resets with every administration, and a hostile one can hollow it without repealing anything. The Senate-confirmed directorship became a kill switch: leave it vacant (as it has been since 2022) and the office cannot fight for itself.

2. Its strongest powers were never exercised

The subpoena authority has never once been used; data has been gathered by negotiation with the regulators who own it — regulators who view the OFR as a rival. An unused power protects no one and offends everyone.

3. Turf, everywhere

The Fed sees monetary-adjacent research as its own; the SEC and CFTC own their data; banks resented paying assessments for their own surveillance. Every institutional neighbor had a reason to want the OFR small.

4. The smoke-detector problem

A monitoring agency’s success is an absence — the crisis that didn’t happen. In calm years it looks like overhead; after a crisis it looks like failure. There is no political moment at which it looks like a bargain, which is why it has now been cut in both directions of the cycle.

5. A complexity organ inside an equilibrium body

The deepest reason: the OFR was built on the premise that the financial system must be mapped and simulated as a network of heterogeneous agents — while the institutions it serves think in equilibrium models where such maps are a curiosity. It was asked to answer questions its principals were never required to ask. The gap between its epistemology and its masters’ is the same gap between complexity economics and the mainstream, made institutional.

IV · The People

Eight figures, chosen because together they are the map from the OFR to complexity theory. Affiliations as of their OFR-era work; current seats noted where they matter.

Allan Mendelowitz

Instigator · CE-NIF co-founder

Training: Economist (PhD, economics); career at GAO, the Export-Import Bank, and as chairman of the Federal Housing Finance Board
Life’s work: Making government capable of measurement — from trade statistics to housing finance to the NIF campaign; now president of the ACTUS Financial Research Foundation
Complexity link: ACTUS — a standard that renders every financial contract as executable, machine-readable logic — is the substrate a full-economy simulation requires. He has spent fifteen years building the data layer for the model Farmer wants to run.

The CE-NIF white papers & Senate testimony 2009–10Takeaway · The founding argument: the crisis was a data failure before it was a policy failure; the fix is an institution, not a rule.
The case for a smart financial contract standard with Brammertz · J. Risk Finance 2018Takeaway · If contracts are algorithms, the financial system is computable — risk aggregation becomes running the code, not surveying the holders.
ACTUS taxonomy & specification actusfrf.org · ongoingTakeaway · Thirty-odd contract types cover nearly all finance; a state-machine per type turns balance sheets into simulable objects — the missing piece between Lane VII’s registry and Lane VI’s generator.

John Liechty

Instigator · CE-NIF co-founder

Training: PhD statistics, Cambridge; professor of marketing and statistics, Penn State (Smeal)
Life’s work: Bayesian computation (MCMC) applied wherever high-dimensional behavior hides — consumer attention, portfolio choice, systemic risk
Complexity link: The computational-statistics case for the OFR: systemic risk is a high-dimensional inference problem, and the state needs the compute and the priors to run it.

Portfolio selection with higher moments with Harvey, Liechty & Müller · Quantitative Finance 2010Takeaway · Once skew and kurtosis matter — and in crises they are all that matters — Bayesian machinery replaces mean-variance; tails are a first-class object.
The NIF proposal & congressional testimony 2009–10Takeaway · The technical blueprint: a reference data facility plus an analytic center — the two halves the OFR statute then codified.
Bayesian models of attention from eye-tracking with Pieters & Wedel · Psychometrika 2003Takeaway · The method travels: latent behavioral states inferred from noisy traces — the same inference shape as reading strategy from AIS trajectories in Lane VIII.

Richard Berner

First director, 2013–2017

Training: PhD economics (Pennsylvania); Fed staff, then chief US economist at Morgan Stanley; now co-director, NYU Stern Volatility and Risk Institute (with Robert Engle)
Life’s work: Converting Wall Street macro-forecasting discipline into official financial-stability monitoring
Complexity link: Institutionalized the monitoring view — dashboards over forecasts, vulnerabilities over point predictions — and won the LEI fight that gave the financial network its node labels.

The OFR Financial Stability Reports & Monitor framework OFR 2013–17Takeaway · Stability is tracked as a vector of vulnerabilities (leverage, liquidity, funding, contagion), not a single risk number — the state adopting a systems view.
Stress testing networks: the case of central counterparties with Cecchetti & Schoenholtz · NBER 2019Takeaway · CCPs concentrated risk rather than removing it; a stress test of a node is meaningless without the network around it.
CRISK: measuring the climate risk exposure of the financial system with Jung & Engle · 2023–25Takeaway · Climate beta estimated from market data, marked to a stress scenario — the nearest official-sector cousin to the climate-transmission lanes.

Richard Bookstaber

Research principal · the ABM champion

Training: PhD economics, MIT; risk chief at Morgan Stanley, Salomon, Moore Capital, Bridgewater; later CRO of the University of California
Life’s work: Arguing — from inside the machine — that crises are made by tight coupling and complexity, and must be modeled agent by agent
Complexity link: The purest complexity economist ever employed by the US government; brought Minsky, fire sales, and agent-based modeling into official stress thinking.

A Demon of Our Own Design book · 2007Takeaway · Written before the crisis it describes: innovation adds coupling, coupling turns local failures into cascades — complexity itself, not any instrument, is the risk.
An agent-based model for financial vulnerability with Paddrik & Tivnan · OFR WP, J. Econ. Interaction & Coordination 2018Takeaway · Banks, dealers, and funds as three agent classes reproduce fire-sale amplification; a stress test becomes a simulation, not a balance-sheet arithmetic.
The End of Theory book · 2017Takeaway · Under radical uncertainty, deductive equilibrium theory fails in principle; agent-based simulation is not an approximation to theory but its replacement for crises.

Mark Flood

Research principal · the data architect

Training: PhD economics (North Carolina); earlier research posts at the St. Louis Fed and FDIC
Life’s work: Financial data as public infrastructure — standards, ontologies, and the formal representation of contracts
Complexity link: The bridge between data engineering and theory: if the system is a network of contracts, then contract representation is systemic-risk methodology.

Monitoring financial stability in a complex world with Mendelowitz & Treacy · 2012Takeaway · The monitoring problem stated as computer science: scale, standards, and latency — not more economists — are the binding constraints.
Handbook of Financial Data and Risk Information ed., Cambridge · 2014Takeaway · The field manual for the data layer — what exists, who holds it, what it can and cannot say about risk.
Contract as automaton with Goodenough · OFR WP, J. Financial Market InfrastructuresTakeaway · A financial agreement is formally a state machine; law becomes computable, and the economy becomes, in principle, executable — ACTUS’s theoretical twin.

Paul Glasserman

Research fellow · the rigorist

Training: PhD applied mathematics, Harvard; Jack R. Anderson Professor, Columbia Business School
Life’s work: Computational probability for finance — Monte Carlo methods, and later the mathematics of contagion and stress
Complexity link: The discipline inside the network literature: his results say when contagion claims are and are not justified — the López de Prado role, played inside the Farmer subject matter.

Monte Carlo Methods in Financial Engineering book · 2003Takeaway · The canon of simulation-based pricing and risk — the numerical spine under every modern stress engine.
How likely is contagion in financial networks? with Young · OFR WP 2014, J. Banking & Finance 2015Takeaway · The sobering bound: direct default cascades alone are surprisingly hard to generate; real amplification needs fire sales, funding runs, confidence — a constraint every network paper must now answer.
Contagion in financial networks with Young · J. Economic Literature 2016Takeaway · The field, organized: which mechanisms are established, which are speculation — the map of what remains provable.

H. Peyton Young

Collaborating theorist

Training: PhD mathematics, Michigan; Oxford (emeritus), LSE, Johns Hopkins
Life’s work: Evolutionary game theory and stochastic stability — how conventions, norms, and institutions emerge and shift
Complexity link: The direct bridge to the conventions research lane: his mathematics is the formal theory of how a convention (a pricing rule, a tariff design, an accounting norm) becomes locked in and how it tips.

The evolution of conventions Econometrica 1993Takeaway · Conventions are stochastically stable equilibria — which convention a society lands on is predictable from the perturbation structure, not from efficiency. The theory under “who pays for weather is a convention.”
How likely is contagion in financial networks? with Glasserman · 2015Takeaway · (As above) — the theorist’s half of the OFR’s most-cited result.
How safe are central counterparties in credit default swap markets? with Paddrik · OFR WP, Mathematics & Financial Economics 2021Takeaway · Using actual supervisory CDS data: a CCP’s safety depends on the network of its members’ other obligations — the node is only as safe as the graph.

Mark Paddrik

Research principal · the simulator

Training: PhD systems & information engineering, Virginia
Life’s work: Micro-level simulation of market infrastructure — order books, margin systems, clearing networks — against real supervisory data
Complexity link: The proof that calibrated, policy-grade ABM is possible inside government when the data access exists — the working demonstration of what the OFR was for.

An agent-based model of the E-Mini S&P 500 and the Flash Crash with Hayes, Todd, Yang, Beling & Scherer · 2012Takeaway · A simulated limit-order book with realistic agent classes reproduces May 6, 2010 — microstructure crises are emergent and rehearsable.
An agent-based model for financial vulnerability with Bookstaber & Tivnan · 2018Takeaway · (As above) — the fire-sale engine.
CCP stress under margin calls with Young & co-authors · OFR seriesTakeaway · Margin procyclicality simulated on real cleared-market data: the risk-management rule itself is the amplifier — a convention producing the distribution, measured.

V · The Map to Complexity Theory

Read as one story, the OFR is the networks-and-systemic-risk school of complexity economics given a government address. Its founding premise (the system is a network no one can see), its best data win (the LEI — names for nodes), its research crown jewels (Glasserman–Young on contagion, Bookstaber–Paddrik–Tivnan on fire sales, Paddrik–Young on CCPs), and its unfinished ambition (ACTUS and contract-as-automaton — the economy as executable code) are complexity economics end to end. It employed the field’s purest practitioner-theorist (Bookstaber), collaborated with its sharpest conventions theorist (Young), and enforced identification discipline (Glasserman) a decade before “causal factor investing” made that fashionable.

Its failure is equally instructive, and it is the strategic lesson of this dossier: the institutional home for complexity finance proved fragile, so the durable home is public, reproducible research — open data, open code, results anyone can rerun. The OFR’s twin dismantlings are the strongest argument that the open seat on the strategy map (physical systems × runs-a-book, published) is not just unoccupied but structurally undersupplied: governments defund it, desks privatize it, academics lack the data. And two of its people hand this program its tools directly — Young’s stochastic stability is the formal theory beneath the valuation-conventions and who-pays-for-weather lanes, and Mendelowitz’s ACTUS is the contract-level substrate that would someday let Lane VII’s observable economy be not merely measured but run.