A principal must decide between two options. Which one she prefers depends on the private information of two agents. One agent always prefers the first option; the other always prefers the second. Transfers are infeasible. One application of this setting is the efficient division of a fixed...

The equation x^a+y^b+z^c=m in real numbers represents a group of Nonlinear Multi-Agent Systems, is an ill-posed problem and can be used in physical sciences and social sciences. This article introduces some Equivalence-Conditions, Existence-Conditions and properties of xa+yb+zc=m in real...

The global energy crisis that began in fall 2021 and the following spike in energy price constitute a major challenge for the world economy which risks undermining the post-COVID-19 recovery. In this paper, we develop and validate a new macroeconomic agent-based model with an endogenous energy...

An agent-based model is developed to address the relationship between the ownership structure of an enterprise and the evolution of its product portfolio. The coherence and evolution of a product portfolio is operationalized by transition rules regarding the Moore environment. The distinguishing...

Though climate physical and transition risks will likely affect socio-economic dynamics along any transition pathways, their unfolding is still poorly understood. This also affects the development of climate-change policies to achieve sustainable growth. In this paper, we discuss a series of...

Soaring energy prices since fall 2021 have prompted European governments to introduce policy measures to support households and businesses. In this paper, we employ the MATRIX model, a multi-sector and multi-agent macroeconomic model calibrated on the Euro Area, to analyze the economic and...

Behavioral Finance has become a challenge to the scientific community. Based on the assumption that behavioral aspects of investors may explain some features of the Stock Market, we propose an agent based model to study quantitatively this relationship. In order to approximate the simulated...

We apply Multi-Agent Deep Reinforcement Learning (MADRL) to inventory management problems with multiple echelons and evaluate MADRL's performance to minimize the overall costs of a supply chain. We also examine whether the upfront-only information-sharing mechanism used in MADRL helps alleviate...