Emergent Strategic Behaviour in a Macroeconomic Agent-Based Model with Reinforcement Learning

Domenico Delli Gatti, Andrea Coletta, Aldo Glielmo, Filippo Gusella, Enrico Maria Turco and Alessia Lo Turco

No 12862, CESifo Working Paper Series from CESifo

Abstract: In canonical macroeconomic agent-based model (ABM), firms pursue behavioural (non-optimal) price and quantity strategies, that take the form of heuristics. In this paper we incorporate reinforcement learning (RL) into an otherwise standard ABM by replacing a fraction of heuristic-using firms with RL agents that learn profitmaximizing strategies through repeated interaction with the economic environment. When RL agents adopt a shared Q-function, they endogenously converge to one of three distinct strategic regimes – market power, predatory pricing, or quasi-perfect competition – with the prevailing equilibrium depending on the degree of market competition and the share of RL agents. Under independent Q-functions, agents spontaneously segregate into heterogeneous strategies, yielding higher aggregate market power and producer surplus without explicit coordination. The prevalence of RL agents shapes aggregate output and volatility in a non-monotonic way. To rationalize these findings, we develop a stylized theoretical framework that links the competition intensity between RL and non-RL agents with the prevailing optimal pricing strategies.

Keywords: macroeconomics; agent-based modelling; reinforcement learning (search for similar items in EconPapers)
JEL-codes: C63 D21 E37 L13 (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.ifo.de/DocDL/cesifo1_wp12862.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_12862

Access Statistics for this paper

More papers in CESifo Working Paper Series from CESifo Contact information at EDIRC.
Bibliographic data for series maintained by Klaus Wohlrabe ().