Principal Investigator: David Tuckett

David TuckketDavid Tuckett trained in Economics, Medical Sociology and Psychoanalysis and is Professor and Director of the Centre for the Study of Decision-Making Uncertainty at UCL in the Faculty of Brain Sciences, as well as a Fellow of the Institute of Psychoanalysis in London. He works part-time in clinical practice but since winning a 2006 Leverhulme Research fellowship for a “psychoanalytic study of investment markets” has been collaborating with a range of colleagues to introduce psychoanalytical understanding to behaviour in the financial markets and the economy more generally.

His book Minding the Markets: An Emotional Finance View of Financial Instability was published in New York and London by Palgrave Macmillan in June 2011 and a further monograph written with Professor Richard Taffler (University of Warwick School of Management) entitled “Fund Management: An Emotional Finance Perspective” was published by the Research Foundation of CFA Institute.

Prior to this, he received the 2007 Sigourney Award for distinguished contributions to the field of psychoanalysis. He has published books and articles in sociology, psychoanalysis, economics, and finance and is a former President of the European Psychoanalytic Federation, Editor in Chief of the International Journal of Psychoanalysis and Principal of the Health Education Studies Unit at the University of Cambridge.

Co-investigators: Prof Laura Bear, Professor Douglas Holmes, & Professor Sir Tim Besley


Summary

David is bringing together two anthropologists (Laura Bear and Douglas Holmes) and a macroeconomist (Tim Besley). They will investigate the psychological factors that affect how the Bank of England conducts monetary policy under uncertainty.

“We will explore how diverse narratives take shape in conversation and how debates unfold in face-to-face encounters with officials of the Bank and in discussions we observe at the policy meetings.”

The researchers will have a unique opportunity to attend Monetary Policy Committee (MPC), Financial Policy Committee (FPC) and other committee meetings, and have discussions with senior economists at the Bank. These committees are presented information on economic trends and conditions from the Bank’s network of over 9000 contacts across business, financial and government communities. But how useful is the information that the Bank committees receive? Can it be relied on for policy making? Does it accurately describe the current state of the economy?

One way of describing how people make decisions is through “narratives.” We form narratives that are subjective ways of organising and understanding the millions of pieces of information we encounter. We use these narratives to help us make decisions in a world of radical uncertainty (i.e. we don’t know all of the possible outcomes in the future).

The hypothesis here is that the agents at the Bank have access to subjective “narratives of action” that drive economic decision making under uncertainty, termed “conviction narratives”. Conviction narratives support individual behaviour and they can change through shifts in shared economic narratives that rise and fall within social networks and the economy as a whole. Do the narratives the Bank gathers though its network affect policy making? And likewise, do the Bank’s communications affect shared narratives?

Additionally, this research will examine if the techniques used to study narratives has potential for the Bank to use the information of its network more efficiently. This would be especially useful when certain conventional economic or financial information is belated or unavailable.

The study will look at what information is presented to the BoE committees and what information the Bank’s agents are collecting. If narrative information drives the economy, then could knowledge of narrative shifts be incorporated into formal macroeconomics and regular policy use? Are some narratives paid more attention to than others?

Part of the innovative challenge of this study is determining how best to elicit narratives to provide a meaningful account of economic and financial conditions. Machine learning and sentiment analysis, among other techniques, will be used for this purpose. As well as potentially improving the efficiency of monetary policy, the methods developed here may also have applications to future macroeconomic questions.


Results

Results will be published here when available.

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