4,565 research outputs found

    Applied Computational Intelligence for finance and economics

    Get PDF
    This article introduces some relevant research works on computational intelligence applied to finance and economics. The objective is to offer an appropriate context and a starting point for those who are new to computational intelligence in finance and economics and to give an overview of the most recent works. A classification with five different main areas is presented. Those areas are related with different applications of the most modern computational intelligence techniques showing a new perspective for approaching finance and economics problems. Each research area is described with several works and applications. Finally, a review of the research works selected for this special issue is given.Publicad

    Econometrics for Learning Agents

    Full text link
    The main goal of this paper is to develop a theory of inference of player valuations from observed data in the generalized second price auction without relying on the Nash equilibrium assumption. Existing work in Economics on inferring agent values from data relies on the assumption that all participant strategies are best responses of the observed play of other players, i.e. they constitute a Nash equilibrium. In this paper, we show how to perform inference relying on a weaker assumption instead: assuming that players are using some form of no-regret learning. Learning outcomes emerged in recent years as an attractive alternative to Nash equilibrium in analyzing game outcomes, modeling players who haven't reached a stable equilibrium, but rather use algorithmic learning, aiming to learn the best way to play from previous observations. In this paper we show how to infer values of players who use algorithmic learning strategies. Such inference is an important first step before we move to testing any learning theoretic behavioral model on auction data. We apply our techniques to a dataset from Microsoft's sponsored search ad auction system

    Market-based Recommendation: Agents that Compete for Consumer Attention

    No full text
    The amount of attention space available for recommending suppliers to consumers on e-commerce sites is typically limited. We present a competitive distributed recommendation mechanism based on adaptive software agents for efficiently allocating the 'consumer attention space', or banners. In the example of an electronic shopping mall, the task is delegated to the individual shops, each of which evaluates the information that is available about the consumer and his or her interests (e.g. keywords, product queries, and available parts of a profile). Shops make a monetary bid in an auction where a limited amount of 'consumer attention space' for the arriving consumer is sold. Each shop is represented by a software agent that bids for each consumer. This allows shops to rapidly adapt their bidding strategy to focus on consumers interested in their offerings. For various basic and simple models for on-line consumers, shops, and profiles, we demonstrate the feasibility of our system by evolutionary simulations as in the field of agent-based computational economics (ACE). We also develop adaptive software agents that learn bidding strategies, based on neural networks and strategy exploration heuristics. Furthermore, we address the commercial and technological advantages of this distributed market-based approach. The mechanism we describe is not limited to the example of the electronic shopping mall, but can easily be extended to other domains

    Optimal Rules for Patent Races

    Get PDF
    There are two important rules in a patent race: what an innovator must accomplish to receive the patent and the allocation of the benefits that flow from the innovation. Most patent races end before R&D is completed and the prize to the innovator is often less than the social benefit of the innovation. We study the optimal combination of prize and minimal accomplishment necessary to obtain a patent in a dynamic multistage innovation race. A planner, who cannot distinguish between competing firms, chooses the innovation stage at which the patent is awarded and the magnitude of the prize to the winner. We examine both social surplus and consumer surplus maximizing patent race rules. We show that a key consideration is the efficiency costs of transfers and of monopoly power to the patentholder. We show that races are undesirable only when efficiency costs are low, firms have similar technologies, and the planner maximizes social surplus. However, in all other circumstances, the optimal policy spurs innovative effort through a race of nontrivial duration. Races are also used to filter out inferior innovators.
    corecore