This paper studies the behavior of price discovery within a context of an agent based stock market in which the twin assumptions namely, rational expectations and the representative agents normally made in mainstream economics, are removed. In this model, traders stochastically update their...

Recent research has shown a variety of computational techniques to describe evolution in an artificial stock market. One can distinguish the techniques based on at which level the learning of agents is modeled. The previous literature describes learning at either individual or social level. The...

This paper uses an agent based financial market calibrated to aggregate data. It shows how these markets are able to magnify the volatility of fundamentals, and to create time series with persistent volatility. The mechanism for this persistence is explored using several of the time series...

The relationship between competitiveness and market performance has been discussed for a long time. In a competitive economic environment, each firm or individual is unable to influence the market. It has been mentioned in the economics courses that the competitive market is more efficient and...

Centralized exchange has a worst-case size-complexity many orders of magnitude lower than decentralized monetary exchange for the same number of agents and goods. A more rapid approach to competitive equilibrium may therefore be possible through centralized exchange. An additional benefit of...

The issue regarding the influence of intelligence on market efficiency has been discussed for a long time. Gode and Sunder (1993) mentioned that the aggregate behavior of zero-intelligence traders is able to generate an efficient market. They introduced two types of markets composed of...

Agent based models take into account limited rational behaviour of individuals acting on financial markets. Explicit simulation of this behaviour and the resulting interaction of individuals provide a description of aggregate financial market time series. At least for some parameter settings,...

This paper presents an adaptive learning model for market-making under the reinforcement learn-ing framework. Reinforcement learning is a learning technique in which agents aim to maximize the long-term accumulated rewards. No knowledge of the market environment, such as the order arrival or...

We examine the dynamic properties of equilibrium stock returns in an incomplete information economy in which the agents need to learn the hidden state of the endowment process. We consider both the case of optimal Bayesian learning and suboptimal learning, including near-rational learning, over-...