It has been difficult to know what does and does not constitute competent psychoanalytic work and so equally difficult to assess when it is being practised and when it is not. This makes difficult any form of disciplined evaluation of the outcome of training, which has a series of problematic outcomes for psychoanalytic practice, psychoanalytic institutions and the relationship to allied disciplines and professions. In this paper, the author considers how far it might be possible to devise aframework for assessment of training programmes within a disciplined psychoanalytic pluralism. The aspiration is to develop a transparent framework, based on an empirically supported demonstration of analytic capacity. The framework needs to be sensitive and subtle, and to be able to withstand challenge. It needs to take cognisance of the twin facts that there is more than one way to practise psychoanalysis and that it is necessary to avoid 'anything goes'. Drawing on an ongoing project undertaken by European IPA institutes, the author describes some of the problems colleagues have been experiencing in European institutes, because they have not had available transparent criteria for assessment. He outlines a preliminary form of a proposed method for making more transparent and supportable assessment. The author intends for this paper to inspire hope, enquiry and debate.

This paper sets out to explore if standard psychoanalytic thinking based on clinical experience can illuminate instability in financial markets and its widespread human consequences. Buying, holding or selling financial assets in conditions of inherent uncertainty and ambiguity, it is argued, necessarily implies an ambivalent emotional and phantasy relationship to them. Based on the evidence of historical accounts, supplemented by some interviewing, the authors suggest a psychoanalytic approach focusing on unconscious phantasy relationships, states of mind, and unconscious group functioning can explain some outstanding questions about financial bubbles which cannot be explained with mainstream economic theories. The authors also suggest some institutional features of financial markets which may ordinarily increase or decrease the likelihood that financial decisions result from splitting off those thoughts which give rise to painful emotions. Splitting would increase the future risk of financial instability and in this respect the theory with which economic agents in such markets approach their work is important. An interdisciplinary theory recognizing and making possible the integration of emotional experience may be more useful to economic agents than the present mainstream theories which contrast rational and irrational decision-making and model them as making consistent decisions on the basis of reasoning alone.

We propose conviction narrative theory (CNT) to broaden decision-making theory in order to better understand and analyse how subjectively means–end rational actors cope in contexts in which the traditional assumptions in decision-making models fail to hold. Conviction narratives enable actors to draw on their beliefs, causal models, and rules of thumb to identify opportunities worth acting on, to simulate the future outcome of their actions, and to feel sufficiently convinced to act. The framework focuses on how narrative and emotion combine to allow actors to deliberate and to select actions that they think will produce the outcomes they desire. It specifies connections between particular emotions and deliberative thought, hypothesising that approach and avoidance emotions evoked during narrative simulation play a crucial role. Two mental states, Divided and Integrated, in which narratives can be formed or updated, are introduced and used to explain some familiar problems that traditional models cannot.

This paper applies algorithmic analysis to financial market text-based data to assess how narratives and sentiment might drive financial system developments. We find changes in emotional content in narratives are highly correlated across data sources and show the formation (and subsequent collapse) of exuberance prior to the global financial crisis. Our metrics also have predictive power for other commonly used indicators of sentiment and appear to influence economic variables. A novel machine learning application also points towards increasing consensus around the strongly positive narrative prior to the crisis. Together, our metrics might help to warn about impending financial system distress.