Marta Posada

Strategic Behaviour in Continuous Double Auction

We analyze with a bottom-up approach the competition between artificial intelligent agents in Continuous Double Auction markets in terms of allocative efficiency, price convergence and emergence or not of Nash equilibriums. In previous works agents have a fixed bidding strategy during the auction, usually under symmetric environments. In our simulations we allow the soft-agents to learn not only about how much they should bid or offer, but also about possible switching between the alternative strategies. We examine the behaviour of strategic traders under general supply and demand schedules (asymmetric environments) thus extending previous results.

Strategic software agents in continuous double auction under dynamic environments

Internet auctions in open dynamic environments have been attracting increasing attention. We analyze with a bottom-up approach the competition between artificial intelligent agents in Continuous Double Auction markets. In almost all previous works agents have a fixed bidding strategy during the auction, usually under static symmetric environments. In our simulations we allow the soft-agents to learn not only about how much they should bid or ask, but also about possible switching between the alternative strategies. We examine the behaviour of strategic traders under dynamic asymmetric environments thus extending previous results. This analysis is important in the design and performance of auctions in the real world (stock exchanges, commodity markets, emission permits, and B2B exchanges) and in the applications of auction theory to many problems in management and production, far beyond market design (market oriented programming).

Taking advantage of solving the resource constrained multi-project scheduling problems using multi-modal genetic algorithms

In this paper, for the first time, multi-modal genetic algorithms (MMGAs) are proposed to optimize the resource constrained multi-project scheduling problem (RCMPSP). In problems where the landscape has both multiple local and global optima, such as the RCMPSP, a MMGAs approach can provide managers with an advantage in decision-making because they can choose between alternative solutions equally good. Alternative optima are achieved because the diversification techniques of MMGAs introduce diversity in population, decreasing the possibility of the optimization process getting caught in a unique local or global optimum. To compare the performance of a MMGAs approach with other alternative approaches, commonly accepted by researchers to solve the RCMPSP such as classical genetic algorithms and dispatching heuristics based on priority rules, we analyse two time-based objective functions (makespan and average percent delay) and three coding systems [random keys (RK), activity list (AL), and a new proposal called priority rule (PR)]. We have found that MMGAs significantly improve the efficacy (the algorithm’s capability to find the best optimum) and the multi-solution-based efficacy (the algorithm’s capability to find multiple optima) of the other two approaches. For makespan the PR is the best code in terms of the efficacy and multi-solution-based efficacy, and the RK is the best code for the average percent delay.

An Artificial Economics View of the Walrasian and Marshallian Stability

The experiments discussed below are an attempt to examine two concepts of instability which stem from two different models of market adjustment used in Economics: Walrasian (W) and Marshallian (M) instability. The M model views volume as adjusting in response to the difference between demand price and supply price at that volume. The W model views price as changing in response to excess demand at that price. Do the M and the W models have a firm foundation on micro-motives, or are they just macro abstractions that we could dispense of in Microeconomics?

Learning in Continuous Double Auction Market

We start from the fact, that individual behaviour is always mediated by social relations. A heuristic is not good or bad, rational or irrational, but only relative to an institutional environment. Thus for a given environment, the Continuous Double Action (CDA) market, we examine the performance of alternative intelligent agents, in terms of market efficiency and individual surplus.

The Effect of Transaction Costs on Artificial Continuous Double Auction Markets

Fast price convergence and high allocative market efficiency (close to 100%) are two of the most robust results in Experimental Economics. When human-subjects are replaced by artificial-agents, high allocative market efficiency is also attained even if the artificial agents have zero intelligence, but price convergence depends on the agents’ learning. In this paper we study the sensitivity of Continuous Double Auction performance to the imposition of monetary costs in the market. We find that transactions costs reduce market efficiency. Price convergence results are very different when the monetary cost is imposed on the transaction or on the submissions to buy or to sell. Our agent-based market model confirms and extends previous Experimental Economics market results, and provides new behavioral explanations of the price dynamics.

Emergence, Culture and Organizational Structure

In this paper, the relationship between structure and culture within an organization is explored using an agent-based approach. In order to analyze how the organizational structure influences the emergence of organizational culture, an agent-based model of bounded-rational agents (who dynamically interact adapting their effort) is proposed. We have found that formal organizational configurations are more likely to favor the emergence of culture than informal interactions.

Rethinking the Social Contract: An Agent-Based Model Approach

The actual crisis has brought to a forefront the failure in wealth distribution and it has questioned market with democracy. It has vividly demonstrated the deficiencies in the outdated current economic theories, calling for rethinking Economics. Agent Based Modelling (ABM) applied to this economic rethinking is a choice, a challenge and a promise. Market efficiency has received attention from ABM experts and the authors. However, they have not studied the wealth distribution problem. In this paper, using an agent-based model approach, we study the sensitivity of wealth distribution to the agents’ behavior in a Continuous Double Auction market. We find that the inequality on wealth distribution increases as the percentage of parasitic agents grows.

Agent-Based Experimental Economics in Signaling Games

In this paper we built up an agent-based model inspired by human-subject behaviour in a signaling game experiment. The behavioural patterns of players observed in the experiment are classified in terms of attitudes, emotions and frugal and fast heuristics. We build up an agent-based model where artificial agents are endowed with these cognitive patterns by means of an endorsement scheme. We show the relationship between cognition and Multi-Agent interaction. We validate the model since it reproduces well the behavioural patterns observed in the human-subjects experiment.