In this paper we present a model of tax compliance with heterogeneous agents who maximize their individual utility based on income and the conjectured level of per capita public expenditure. We formally include psychological drivers in this model. These drivers affect individual behavior, such...

This research aims to investigate, through simulation models, how the interaction among agents in an artificial stock market can affect the dynamics of asset prices. Thus, the study follows a different methodology for the analysis of prices by exploring the simulation of agents' behavior in an...

We develop an agent-based model in which heterogenous and boundedly rational agents interact by trading a risky asset at an endogenously set price. Agents are endowed with balance sheets comprising the risky asset as well as cash on the asset side and equity capital as well as debt on the...

The aim of this paper is to investigate the relationship between increasing inequality and financial fragility in an agent based macroeconomic model. We analyse the effects of a non-linear relationship between wealth and consumption on the evolution of the economic system. Preliminary results...

We build an agent-based model populated by households with heterogenous and time-varying financial conditions in order to study how different inequality shocks affect income dynamics and the effects of different types of fiscal policy responses. We show that inequality shocks generate...

We present an Agent-Based Stock Flow Consistent Multi-Country model of a Currency Union to analyze the impact of changes in the fiscal regime of member countries, that is permanent changes in the deficit-to-GDP targets that governments commit to comply. Simulations are performed under three...

This paper uses a macroeconomic agent-based model building on Delli Gatti et al. (2011) to investigate the influence of agents' expectations and consumption choices on government expenditure multipliers. Following a thorough investigation of the size of the multiplier in the pre-existing...

A new algorithm for calibrating agent-based models is proposed, which employs a popular gradient boosting framework. Machine learning techniques are not used to develop a surrogate model, but rather assist in narrowing down the parameter space during the search for optimal parameters. Our...

This paper presents an adapted agent-based model for the diffusion of new aircraft model series. Expanding on the classical economic decision framework, where investment decision-making is entirely based on profitability, our holistic modeling approach takes into account profitability,...