Mingli Chen, Andreas Joseph*, Michael Kumhof* & Aruhan Shi
In our project for Rebuilding Macroeconomics, we bring together two principal ideas into a standard macro model: money creation as unconstrained by physical savings, and replacing perfect rationality with artificial intelligence (AI) to model agents’ expectations.
Financial stability has been at the centre of economic policy discussions since the Global Financial Crisis (GFC). Prior to the crisis, finance as a large sector of the economy, as it is in the UK, had rarely been substantively incorporated into mainstream macroeconomic models. Since the crisis, a growing number of models consider the financial sector and its interactions with the rest of the economy in greater detail to better represent an underlying complex system.
The standard approach to incorporating finance treats banks as intermediaries of physical resources between savers and borrowers – essentially as ‘intertemporal commodity traders’. In reality, when a customer requests a loan, the bank does not check if there are sufficient funds in the vault. Instead it assesses the riskiness of the loan on a set of given criteria and takes a decision on whether to create a simultaneous new deposit for the borrower.
This deposit, a debt of the bank, is created purely as a digital book-keeping entry, with no role for physical resources. Unlike the debts of other economic agents, bank deposits are generally acceptable as a medium of exchange, principally because of central bank and regulatory support that ensures trust in the banking system. By the same principle, banks may abruptly halt the provision of credit reversing this process of money creation.
This can be seen in Figure 1, showing the annual percentage change of private sector credit in the US. There was a ‘cliff edge’ in credit provisioning at the onset of the GFC. Standard macro models have a hard time describing such large and sudden moves.
Figure 1: Credit dynamics in the US around the Global Financial Crisis. Source: BIS
However, models which allow for the creation of money, lead to better fits to observed economic dynamics. In our project banks are integrated within a New Keynesian macroeconomic framework in a way that reflects their ability to create money in this manner.
But how do such sudden changes in behaviour, like the above contraction in credit provision, come about? They are caused by abrupt changes in expectations regarding the economic outlook.
This insight came with the introduction of micro-foundations and expectations formation into macroeconomics. However, on the other hand, the standard paradigm of rational expectations under full information, while analytically simple, has come under growing scrutiny due to its unrealistic assumptions. On the other hand, behavioural rules, e.g. the use of simple heuristics to take decisions, can lead to impressive results in modelling micro behaviour but may be judged as arbitrary and as imposing insufficient discipline on a macro model.
The number of possible decision rules may be large in problems involving many variables. We propose the introduction of a middle ground, which may also allow us to uncover decision rules in a rigorous framework.
An underexplored dimension of agents’ behaviour, which is making ground in macroeconomics, is learning. In learning models, households, consumers or firms, need time to adjust to new realities and make (understandable) mistakes in their assessment of the future, but adjust their behaviour as new information becomes available.
In this project, we propose to take this methodology further from the current approaches used in macroeconomic models by incorporating AI techniques to learning and decision processes. In this framework, an AI agent interacts with the environment and chooses an action. This action affects how the current state transitions to the next one, i.e. how the world moves on. In this process, every agent receives rewards or punishments based on their actions, which is then taken into consideration for the future.
Agents’ forward-looking behaviour is based on the revision of past expectations on the basis of observed outcomes. Not only is this framework intuitive and realistic, it also has technical advantages over previous learning approaches in macroeconomics. For example, it introduces a lever which allows us to ‘tune the rationality of agents’. The choice of the desired, or right level, of this tuning is an empirical question and of course may change over time if new information challenges our present understanding. Another advantage is that this approach is able to handle a much larger number of state variables than traditional learning models.
We are interested in applying the two key building blocks of money creation and AI agents to the setting of the New Keynesian Dynamic Stochastic General Equilibrium models to model the dynamics of an economy that copes with large shocks. This will enable us to answer some important and timely policy relevant questions: how do AI agents respond to unexpected shocks and how do they differ from currently used rational benchmarks for policy assessment? What are the implications for credit provision, money creation and financial stability?
For instance, given a Lehman Brothers-type event, the reaction of agents will depend on whether and how much they have learned about such an event beforehand. The money creation model by itself can generate extremely large responses to such events, but whether they actually do occur depends on how fast banks and their customers learn and respond. The proposed framework allows us to study this situation in a rigorous framework while incorporating realistic mechanisms for money creation and expectations formation.
*Disclaimer: The views expressed here are those of the authors alone and do not necessarily reflect those of the Bank of England or any of its committees.