Companies in the S&P 500 produce quarterly financial statements that are closely studied by investors who forecast the stock market performance of those companies. This paper describes a Neural Logic Network (NLN) for predicting stock market returns based on financial ratios from financial...

Intelligent agents and multi-agent systems prove to be a promising paradigm for solving problems in a distributed, cooperative way. Neural networks are a classical solution for ensuring the learning ability of agents. In this paper, we analyse a multi-agent system where agents use different...

This paper presents a neural network model developed to simulate the endogenous emergence of bounded-rational behavior in normal-form games. There exists an algorithm which, if learnt by a neural network, would enable it to perfectly select Nash equilibria in never before seen games. However,...

This paper presents a novel method for the forecasting of mean hourly wind speed data using time series analysis. The initial point for this approach is mainly the fact that none of the forecasting approaches for hourly data, that can be found in the literature, based on time series analysis or...

This paper considers the use of neural networks to model bounded rational behaviour. The underlying theory and use of neural networks is now a component of various forms of scientific enquiry, be it modelling artificial intelligence, developing better pattern recognition or solving complex...

Companies in the S&P 500 produce quarterly financial statements that are closely studied by investors who forecast the stock market performance of those companies. This paper describes a Neural Logic Network (NLN) for predicting stock market returns based on financial ratios from financial...

In this methodological work I explore the possibility of explicitly modelling expec-tations conditioning the R&D decisions of firms. In order to isolate this problem from the controversies of cognitive science, I propose a black box strategy through the concept of internal model . The last part...

This paper addresses the question of whether neural networks, a realistic cognitive model of the human information processing, can learn to backward induce in a two stage game with a unique subgame-perfect Nash Equilibrium. The result that the neural networks only learn a heuristic that...

This paper presents a neural network based methodology for examining the learning of game-playing rules in never-before seen games. A network is trained to pick Nash equilibria in a set of games and then released to play a larger set of new games. While faultlessly selecting Nash equilibria in...