Building ABMs to Control the Emergence of Crisis Analyzing Agents' Behavior

Abstract

Agent-based models (ABMs) are quite new in the modeling landscape; they emerged on the scene in the 1990s. ABMs have a clear advantage over other approaches: they create the capacity to manage learning processes in agents and discover novelties in their behavior. In addition to bounded rationality assumptions, ABMs share a number of peculiar characteristics: first of all, a bottom-up perspective is assumed where the properties of macro-dynamics are emergent properties of micro-dynamics involving individuals as heterogeneous agents who live in complex systems that evolve through time. To apply this framework to financial crisis analysis, a simplified implementation of the SWARM protocol (www.swarm.org), based on Python, is introduced. The result is the Swarm-Like Agent Protocol in Python (SLAPP). Using SLAPP, it is possible to focus on natural phenomena and social behavior. In the case of this chapter, the authors focus on the banking system, recreating the interactions of a community of financial institutions that act in the payment system and in the interbank market for short-term liquidity.