Macroeconomics with Human Sentient and Social Actors

1. Macroeconomics with Human Sentient and Social Actors David Tuckett , Paul Ormerod, Robert Smith and Rickard Nyman Centre for the Study of Decision-Making Uncertainty University College, London

2. Human Sentient Agents • An economy is a complex system populated by thinking human beings who have co-evolved in human groups. • Goal of macroeconomics is to understand the conditions under which economies grow or contract or their future rates of inflation, unemployment and capital utilization and formation and to give advice about the impact on them of particular fiscal and monetary policies or shocks. • To do it we postulate models with mechanical agents in quite simple systems with little interaction or uncertainty. • Models exclude the feared complications and loss of rigour that are introduced by: • Radical uncertainty • Human Psychology and Neurobiology • Social Life • In what ways to understand what issues in the economy can we construct models with human sentient and social actors that work better?

3. Information under Ontological Uncertainty • Modelling of agent behavior has depended on the assumption that information is non-ambiguous and present or absent • no special processes of perception and interpretation • Psychology  irrationality and bias. • Psychologically or socially driven action introduces individualistic and idiosyncratic impulses disconnected from any fundamental considerations that might drive outcomes or as herding • But in social and psychological science agents can mostly only discover fundamentals through constructingthe meaning of the information and drawing on a wide range of adaptive capacities and social signals to do so. • Ontological uncertainty about information meaning occurs when decisions have to be made in situations with a long term time horizon, when decisions are non-reversible and in non-routine situations. • In such situations action to obtain gain necessarily incurs potential loss – constituting an inherent cognitive and emotional conflict between action and inertia. The question: how does an agent manage to act at all?

4. Emotion, Simulation and Narrative • What are emotions for? An evolutionary survival resource – to treat them as error/bias is a mistaken simplification. • Faced with uncertainty actors seek to gain controlover the situation facing them through time and thy do this using their human capacity for neurobiological simulation and establishing a sense of truth. • Narrative is a core human capacity for constructing and making sense of the world and communicating about it. • It is built in to the development of brain structures and language to process complex situations (e.g. life in a family and the world) • Paradigmatic and Narrative sense of truth (Jerome Bruner) • By definition narratives leave things out.

5. Conviction Narrative Theory (CNT) • CNT is a theory of how agents use human capacity to face the future by gaining confidence to act at all (animal spirits – spontaneous urge to action). • Overcome inertia and anxiety by engaging neurobiologically based affective, attachment and bonding system. • CN’s developed by simulating the state of the organism (body) in future, managing conflict by “creating” enough “certainty” to act. • Conviction can be achieved by eviscerating doubt – phantastic object narratives (PON), divided states (DS), groupfeel (GF). • CNs are socially embedded. • CNs are psychologically invariant – must generate more E than A.  emotion in an economy or group not just individual or random noise but an indicator. •  CNT can be studied through ethnographic observation, interview data and document analysis.

6. Directed Algorithmic Text Analysis (DATA) • Algorithm based unstructured document analysis. • Detect shifts in relations between Anxiety and Excitement in CN through time. • Procedure: • Text with date stamp and (ideally) “To” and “”From” labels (networks) • Identify “excitement” (attractor) words and anxiety-doubt (words) – (Doubt repellers). • Count them. • Track shifts in relationship through time. • Apply statistical tests.

7. Source – Reuters News Archive

8. UK • The animal spirits series in both the US and the UK appear to have given clear prior warning of the downturn, well in advance of any other indicators. In June 2007, the value fell sharply in both economies, to -0.113 in the US and -0.406 in the UK. Compared to the mean values January 2004 through May 2007, these are, respectively, 2.55 and 3.49 standard deviations below- pre-dates that of existing survey measures by several months.

9. Fannie Mae Sentiment Shifts 2003-2013 Text Source – Reuters News Archive

10. Using Economic Consensus Forecasts to Predict Preliminary Monthly MCI

11. Using Relative Sentiment Shift Measure to Predict Preliminary Monthly MCI. Source: Brokers Reports.

13. New Data Sources • Bloomberg, HSBC, Reuters broker reports and economic commentary. • Bank of England Market Intelligence or other organizational documents. • Email. • Interview transcripts – eg BoE agents, Regulatory inspectors, risk surveys. • Economic and Financial blogs, social media.

14. More Advanced Analysis • Better specification of shifts in target emotions beyond word lists. (Sematic and Syntactic analysis etc.?) • Introduction of network dynamics. • Better selection of narratives expected to correlate with future plans. • Correlations with measures of capital formation, innovation etc. • New more complex clustering of words.