We model the organization of the firm as a type of artificial neural network in a duopoly framework. The firm plays a repeated Prisoner’s Dilemma type game, but also must learn to map environmental signals to demand parameters. We study the prospects for cooperation given the need for the firm to learn the environment and its rival’s output. We show how a firm’s profit and cooperation rates are...

We present an agent-based model of endogenous merger formation in a market with turnover of market participants. We describe the dynamics of the model and identify the conditions under which market competition is sufficiently disrupted to prompt extended periods during which mergers are desirable. We also demonstrate how merger waves can be triggered by industry shocks and firm overconfidence. ...

We study a model in which individual agents use simple linear first order price forecasting rules, adapting them to the complex evolving market environment with a smart Genetic Algorithm optimization procedure. The novelties are: (1) a parsimonious experimental foundation of individual forecasting behaviour; (2) an explanation of individual and aggregate behavior in four different experimental ...

This paper describes a suite of MATLAB® routines devised to provide an approximately optimal solution to an infinite-horizon stochastic optimal control problem. The suite is an updated version of that described in [1] and [2]. Its routines implement a policy improvement algorithm to optimise a Markov decision chain approximating the original control problem, as described in [3]. 2014 Working Pa...

Recently, there has been a renewed interest in modeling economic time series by vector autoregressive moving-average models. However, this class of models has been unpopular in practice because of estimation problems and the complexity of the identification stage. These disadvantages could have led to the dominant use of vector autoregressive models in macroeconomic research. In this paper, sev...

The stochastic modeling and determination of reserves and risk capitals for variable annuity guarantee products are relatively new developments in the insurance industry. The current market practice is largely based on Monte Carlo simulations, which have great engineering flexibility but the demand for heavy computational power can be prohibitive in many cases. In this paper, we distinguish and...

This paper is aimed at investigating the effects of government intervention through unemployment benefits on macroeconomic dynamics in an agent-based decentralized matching framework. The major result is that the presence of such a public intervention in the economy stabilizes the aggregate demand and the financial conditions of the system at the cost of a modest increase of both the inflation ...

In this paper we argue that in realistically calibrated two period general equilibrium models with incomplete markets CAPM-pricing provides a good benchmark for equilibrium prices even when agents are not mean-variance optimizers and returns are not normally distributed. We numerically approximate equilibria for a variety of di erent speci cations for preferences, endowments and dividends and c...

Options are believed to contain unique information on the risk-neutral moment generating function (MGF) or the risk-neutral probability density function (PDF) of the underlying asset. This paper applies the wavelet method to approximate the implied risk-neutral MGF from option prices. Monte Carlo simulations are carried out to show how the risk-neutral MGF can be obtained using the wavelet meth...

This paper describes a simulation approach for modelling decisionmaking processes under incomplete and imperfect information in Agentbased Computational Economics (ACE). The main idea is to represent decision-making in a model-free framework that can be applied to a larger set of simulation problems, not just the domain modelled. The method translates some basic sociopsychological concepts from...