As a response to a changing labour market scenario and to the concerns for increasing costs and bad incentives of traditional income support policies, the last decades have witnessed, in many countries, reforms introducing more sophisticated designs of means-testing, eligibility and tagging. In...

Convertible bonds are an important segment of the corporate bond market, however, as hybrid instruments, convertible bonds are difficult to value because they depend on variables related to the underlying stock, the fixed-income part, and the interaction between these components. Besides,...

Mitigation-Process Integrated Assessment Models (MP-IAMs) are used to analyze long-term transformation pathways of the energy system required to achieve stringent climate change mitigation targets. Due to their substantial temporal and spatial aggregation, IAMs cannot explicitly represent all...

This article aims to develop the theoretical overlapping generation models with the unstable healthcare system due to the problem of missing trading markets between young and old generations. The main healthcare system in Thailand is Universal Coverage (UC) which covers around 48 million Thai...

We present a numerically efficient approach for machine-learning a risk-neutral measure for paths of simulated spot and option prices up to a finite horizon under convex transaction costs and convex trading constraints. This approach can then be used to implement a stochastic implied volatility...

Various approaches used in Agent-based Computational Economics (ACE) to model endogenously determined interactions between agents are discussed. This concerns models in which agents not only (learn how to) play some (market or other) game, but also (learn to) decide with whom to do that (or not).

A competitive exception learning algorithm for finding a non-linear mapping is proposed which puts the emphasis on the discovery of the important exceptions rather than the main rules. To do so,we first cluster the output space using a competitive fuzzy clustering algorithm and derive a fuzzy...

While payoff-based learning models are almost exclusively devised for finite action games, where players can test every action, it is harder to design such learning processes for continuous games. We construct a stochastic learning rule, designed for games with continuous action sets, which...

This paper examines the learning dynamics of boundedly rational agents, who are asked to voluntarily contribute to a discrete public good. In an incomplete information setting, we discuss contribution games and subscription games, the latter including a money-back guarantee in case of provision...