Toward an Autonomous-Agents Inspired Economic Analysis

This paper demonstrates the potential role of autonomous agents in economic theory. We first dispatch autonomous agents, built by genetic programming, to double auction markets. We then study the bargaining strategies discovered by them, and from there an autonomous-agent-inspired economic theory with regard to the optimal procrastination is derived.Agent-Based Double Auction Markets, Autonomous Agents, Genetic Programming, Bargaining Strategies, Monopsony, Procrastination Strategy

Agent-Based Modeling of the Prediction Markets

We propose a simple agent-based model of the political election prediction market which reflects the intrinsic feature of the prediction market as an information aggregation mechanism. Each agent has a vote, and all agents’ votes determine the election result. Some of the agents participate in the prediction market. Agents form their beliefs by observing their neighbors’ voting disposition, and trade with these beliefs by following some forms of the zero-intelligence strategy. In this model, the mean price of the market is used as a forecast of the election result. We study the effect of the radius of agents’ neighborhood and the geographical distribution of information on the prediction accuracy. In addition, we also identify one of the mechanisms which can replicate the favorite-longshot bias, a stylized fact in the prediction market. This model can then provide a framework for further analysis on the prediction market when market participants have more sophisticated trading behavior.Prediction market, Agent-based simulation, Information aggregation mechanism, Prediction accuracy, Zero-intelligence agents, Favorite-longshot bias

Agent-Based Modeling of the El Farol Bar Problem

In this paper, we study the self-coordination problem as demonstrated by the well-known El Farol problem (Arthur, 1994), which has later become what is known as the minority game in the econophysics community. While the El Farol problem or the minority game has been studied for almost two decades, existing studies are mostly only concerned with efficiency. The equality issue, however, has been largely neglected. In this paper, we build an agent-based model to study both efficiency and equality and ask whether a decentralized society can ever possibly self-coordinate a result with the highest efficiency while also maintaining the highest degree of equality. Our agent-based model shows the possibility of achieving this social optimum. The two key determinants to make this happen are social preferences and social networks. Hence, not only doe institutions (networks) matter, but individual characteristics (preferences) also matter. The latter are open to human-subject experiments for further examination.El Farol Bar problem, Social Preferences, Social Networks, Self-Organization, Emergence of Coordination.

Quantum Information Approach to Rotating Bose-Einstein Condensate

We investigate the 2D weakly interacting Bose-Einstein condensate in a rotating trap by the tools of quantum information theory. The critical exponents of the ground state fidelity susceptibility and the correlation length of the system are obtained for the quantum phase transition when the frst vortex is formed. We also find the single-particle entanglement can be an indicator of the angular momentums for some real ground states. The single-particle entanglement of fractional quantum Hall states such as Laughlin state and Pfaffian state is also studied.Comment: 4 pages, 6 figures, minimal changes are mad

Social Norm, Costly Punishment and the Evolution to Cooperation

Both laboratory and field evidence suggest that people tend to voluntarily incur costs to punish non-cooperators. While costly punishment typically reduces the average payoff as well as promotes cooperation. Why does the costly punishment evolve? We study the role of punishment in cooperation promotion within a two-level evolution framework of individual strategies and social norms. In a population with certain social norm, players update their strategies according to the payoff differences among different strategies. In a longer horizon, the evolution of social norm may be driven by the average payoffs of all members of the society. Norms differ in whether they allow or do not allow for the punishment action as part of strategies, and, for the former, they further differ in whether they encourage or do not encourage the punishment action. The strategy dynamics are articulated under different social norms. It is found that costly punishment does contribute to the evolution toward cooperation. Not only does the attraction basin of cooperative evolutionary stable state (CESS) become larger, but also the convergence speed to CESS is faster. These two properties are further enhanced if the punishment action is encouraged by the social norm. This model can be used to explain the widespread existence of costly punishment in human society.social norm; costly punishment; cooperative evolutionary stable state; attraction basin; convergence speed

A Naturally Minute Quantum Correction to the Cosmological Constant Descended from the Hierarchy

We demonstrate that an extremely small but positive quantum correction, or the Casimir energy, to the cosmological constant can arise from a massive bulk fermion field in the Randall-Sundrum model. Specifically, a cosmological constant doubly descended from the Planck-electroweak hierarchy and as minute as the observed dark energy scale can be naturally achieved without fine-tuning of the bulk fermion mass. To ensure the stabilization of the system, we discuss two stabilization mechanisms under this setup. It is found that the Goldberger-Wise mechanism can be successfully introduced in the presence of a massive bulk fermion, without spoiling the smallness of the quantum correction.Comment: 5 page

Business intelligence in risk management: Some recent progresses

Risk management has become a vital topic both in academia and practice during the past several decades. Most business intelligence tools have been used to enhance risk management, and the risk management tools have benefited from business intelligence approaches. This introductory article provides a review of the state-of-the-art research in business intelligence in risk management, and of the work that has been accepted for publication in this issue of Information Sciences

Failure of Genetic-Programming Induced Trading Strategies: Distinguishing between Efficient Markets and Inefficient Algorithms

The original publication is available at www.springerlink.comOver the last decade, numerous papers have investigated the use of Genetic Programming (GP) for creating financial trading strategies. Typically, in the literature, the results are inconclusive but the investigators always suggest the possibility of further improvements, leaving the conclusion regarding the effectiveness of GP undecided. In this paper, we discuss a series of pretests aimed at giving more clear-cut answers as to whether GP can be effective with the training data at hand. Precisely, pretesting allows us to distinguish between a failure due to the market being efficient or due to GP being inefficient. The basic idea here is to compare GP with several variants of random searches and random trading behaviors having well-defined characteristics. In particular, if the outcomes of the pretests reveal no statistical evidence that GP possesses a predictive ability superior to a random search or a random trading behavior, then this suggests to us that there is no point in investing further resources in GP. The analysis is illustrated with GP-evolved strategies for nine markets exhibiting various trends

Pretests for genetic-programming evolved trading programs: “zero-intelligence” strategies and lottery trading

The original publication is available at www.springerlink.com ; ISBN 978-3-540-46484-6 ; ISSN 0302-9743 (Print) 1611-3349 (Online)International audienceOver the last decade, numerous papers have investigated the use of GP for creating financial trading strategies. Typically in the literature results are inconclusive but the investigators always suggest the possibility of further improvements, leaving the conclusion regarding the effectiveness of GP undecided. In this paper, we discuss a series of pretests, based on several variants of random search, aiming at giving more clearcut answers on whether a GP scheme, or any other machine-learning technique, can be effective with the training data at hand. The analysis is illustrated with GP-evolved strategies for three stock exchanges exhibiting different trends