In this paper, we develop and examine a simple interactive agent-based model, where the distribution of returns generated from the model takes into account two stylized facts about financial markets： fat tails and volatility clustering. Our results indicate that the risk tolerance of...

In order to verify the effects of machine learning in a market structure, an evolutionary model containing firms that use a genetic algorithm to decide their investment in innovative R&D was developed. These firms share the market, with two other types of firms, those with a fixed rate of...

This article tries to connect two separate strands of literature concerning genetic algorithms. On the one hand, extensive research took place in mathematics and closely related sciences in order to find out more about the properties of genetic algorithms as stochastic processes. On the other...

This paper tries to connect the theory of genetic algorithm (GA) learning to evolutionary game theory. It is shown that economic learning via genetic algorithms can be described as a specific form of evolutionary game. It will be pointed out that GA learning results in a series of near Nash...

Innovations, be they radical new products or technology improvements are widely recognized as a key factor of economic growth. To identify the factors triggering innovative activities is a main concern for economic theory and empirical analysis. As the number of hypotheses is large, the process...

Several approaches for subset recovery and improved forecasting accuracy have been proposed and studied. One way is to apply a regularization strategy and solve the model selection task as a continuous optimization problem. One of the most popular approaches in this research field is given by...

This study presents an experimental approach to strategic behavior and economic learning by integrating game theory and Genetic Algorithms in a novel heuristic-based simulation model. Inspired by strategic scenarios that change over time, we propose a model where games can change based on...

This study presents an experimental approach to strategic behavior and economic learning by integrating game theory and Genetic Algorithms in a novel heuristic-based simulation model. Inspired by strategic scenarios that change over time, we propose a model where games can change based on...

A family of Exponentially Fitted Block Backward Differentiation Formulas (EFBBDFs) whose coefficients depend on a parameter and step-size is developed and implemented on the Black-Scholes partial differential equation (PDE) for the valuation of options on a non-dividend-paying stock. Specific...

In recent years, some authors have warned of the increasingly widespread use of risk management techniques by financial institutions, arguing that this can cause the market to become more unstable. To analyse these claims, we present a model based on evolutionary game theory of a financial...