Cybernetic regulation in the age of algorithmic finance
March 26, 2014
A lawyer, an economist and a Python programer walk into a bar. This is no joke. It is the future of financial regulation, at least according to a provocative proposal by Andrei Kirilenko, Professor of the Practice of Finance at the MIT Sloan School of Management.
At the recent meeting on the future of financial standards, jointly organized by SWIFT and the London School of Economics and Political Science, Kirilenko spoke of one of the fundamental problems of modern financial regulation: the translation of legal requirements into the computer code that drives much of the activity in today’s markets. Specifically, when can we say that a code is compliant?
For Kirilenko and his colleagues, this question is critical to how the financial services industry behaves both in the short and long run: a firm that over-interprets regulation may invest more resources in compliance than are necessary; a firm that interprets the rules too weakly may, conversely, risk becoming the object of fines.
To address this, Kirilenko advocates what is best described as ‘coding’ regulation. In addition to (or perhaps, in substitution of) standard cost-benefit analyses, Kirilenko proposes adding a section to regulatory documents that specifies how novel rules operate in terms of Boolean logic. If. Then. Zeroes. Ones. Rules are, after all, instructions that “allow some things, prohibit others, and are neutral over a range of possible values.” They are, hence, logical gates within a normative circuit board. New financial regulations can thus be represented as specific configurations of circuits linking Boolean gates to form a mesh of decisions, possibilities and potential outcomes. And in aid of implementation, such circuits should be made as explicit as possible (they are, after all,more practical than cost benefit analysis), thereby resolving the problem of the under-determination of compliance in code.
Capturing rules, however, requires translating ‘legalese’ into some form of high-level logic, and for this Kirilenko proposes putting lawyers in direct conversation with coders, programmers and systems developers. The outcome of this conversation should be a logical representation of the rules made by lawyers and regulators in a form that can be applied to specific real-world technical systems. A cybernetic reconstruction of the natural language of the legal domain translated into a flowchart out of which code can be developed. (Note that, in a sense, this cybernetic transformation evokes Philip Mirowski’s notion of markomata in the regulatory domain).
Kirilenko’s idea is certainly tantalizing, not the least for sociologists of knowledge, markets and organizations. From an infrastructural perspective, it stresses the importance of inversion, of raising and making visible the plumbing and tracks of finance in order to regulate and change the market (and, interestingly, it calls for an explicit link between legal infrastructures, market engineers, and standard-setters). In epistemic terms, the proposal invites recombining knowledge within finance, creating spaces similar to Galison’s trading zones where communities bearing different languages (e.g. lawyers and technologists) can exchange, modify and build upon specific designs on rules and regulations. Similarly, from an organizational perspective, the idea of coding natural language into Boolean gates highlights the importance of interactional expertise in the regulation of finance: regulators and lawyers need to be able to converse code, just as much as coders need to be able to understand the basics of law and regulatory regimes. If carried to its consequences, this bold proposal necessitates re-conceptualizing how knowledge is created, distributed and organized in financial institutions.
A number of issues remain unsolved about Kirilenko’s proposal (which he aptly calls ‘Financial Regulation 2.0’). The Wittgensteinian critique of rule following, for instance, questions the possibilities of translation and highlights, perhaps, the limits of transforming natural language statements into Boolean relations. Law and regulation are as much about the formal content of texts as they are about their interpretation, indexicality and judgment: indeed, an important part of the work of regulators is justifying rule-breaking (as seen, for instance, in the production of letters of no-action, a fact highlighted by another conference participant). Of course, sociological questions also arise around the process of transforming flowcharts into computer codes: coding is perhaps well described through Andrew Pickering’s metaphor of the mangle, with programmers negotiating their practices and implementations with the affordances and material resistance of the systems on which they work. Will programmers working on different systems, languages and beholding different hacking cultures create the same code out of the same flowchart?
Reservations notwithstanding, Kirilenko’s call for a sort of ‘algorithmic regulation’ remains a fascinating experiment (one that, as far as I could see, is being reproduced across the world): after all, Kirilenko has a unique combination of experience and expertise that places him in a privileged position to talk about finance, technology and regulation. Not only is he an accomplished academic, but he also served as Chief Economist for the Commodities Futures Trading Commission between 2008 and 2012—a period that saw, among other things, the Flash Crash of 2010. Perhaps fire is best fought with fire, and coding regulation is a better mechanism for dealing with a complex market ecology of humans, algorithms and machines than the more traditional forms of rule making. Then again, perhaps it is not. In any case, Kirilenko reminds us that these are certainly interesting (cybernetic) times for the social studies of finance.