Banks are starting to realize that agent-based models (ABMs) give a more robust view of the financial system and could enhance existing modeling techniques used across their organization.
However, utilizing these techniques often requires a team of quants with machine learning skills and experience in computational simulation—and this isn’t an insignificant challenge. Nevertheless, ABMs offer several benefits over traditional modeling techniques which justify the investment.
1. Emergent behavior
The single most powerful feature of ABMs is that the individual actions of the agents combine to produce macroscopic behavior.
The most important example of emergent behavior in economics and financial markets is Adam Smith’s metaphor of the invisible hand: how the self-interested actions of real agents in the economy combine to produce socially optimal outcomes.
One of the strengths of ABM is that this invisible hand is made visible and its workings examined. This is in contrast to some other model approaches in which the actions of many individuals are assumed to lead to a particular outcome, often using a single representative agent. This simplification is valid in some cases, but not all
combinations of behaviors can be represented by the actions of a single agent.
As individual agents are modeled, it becomes possible to explore the consequences of the heterogeneity of the agents; that is, agents being different in some way, perhaps by income, preferences, education or productivity. Incorporating heterogeneity allows for the modeling of much richer behavior.
Inequality is a good example: aggregate wealth can increase, but if it is only a small fraction of the population driving this phenomenon, it would suggest very different underlying economic reasons and consequences for a bank than if the entire population were becoming wealthier.
3. Stylized facts
A stylized fact is a term used in economics and finance to refer to empirical findings that are so consistent (for example, across a wide range of instruments, markets and time periods) that they are accepted as truth. Due to their generality, they are often qualitative.
Perhaps the greatest success of ABMs in economics is explaining the stylized facts observed in asset markets. There are a number of phenomena observed empirically in the markets for assets such as bonds or equities which are not explained by traditional economic theory. Some of the two most widely seen across markets are clustered volatility and ‘fat tails’. ABMs expose the inner workings of these phenomena to scrutiny by risk managers.
4. Realistic behaviors
The generation of realistic behavior, based on observed behavior, is a strength of agent-based models. Research in behavioral economics shows that people often use heuristics when making decisions and that they are not fully rational. There are several models which explore what happens when purely rational options are not available or
are too costly, or when agents’ environments change over time.
5. Exploring the possibilities
One of the advantages of ABM is that it can very efficiently explore a large number of possibilities. Probabilistic rules applied to each individual agent, in turn, can be a simpler way of exploring scenarios than working out how the entire population of agents should behave together.
6. Complexity, non-linearity, and multiple equilibria
A strength of ABMs is that they describe complex systems. Complex systems are characterized by having many interconnected parts, as well as variables which can change dramatically and which can demonstrate self-organization. Additionally, complex systems can undergo sudden, dramatic transitions, sometimes called phase
Recent work on agent-based models of the macro-economy has described phase transitions between low and high unemployment. Every economy displays many of the characteristics associated with complex systems.
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