Agent-based simulation of power exchange with heterogeneous production companies

Since early nineties, worldwide production and distribution of electricity has been characterized by a progressive liberalization. The state-owned monopolistic production of electricity has been substituted by organized power exchanges (PEs). PEs are markets which aggregate the effective supply and demand of electricity. Usually spot-price market are Day Ahead Market (DAM) and are requested in order to provide an indication for the hourly unit commitment. This first session of the complex daily energy market collects and orders all the offers, determining the market price by matching the cumulative demand and supply curves for every hour of the day after according to a merit order rule. Subsequent market sessions (also online) operate in order to guarantee the feasibility and the security of this plan. The electric market is usually characterized by a reduced number of competitors, thus oligopolistic scenario may arise. Understanding how electricity prices depend on oligopolistic behavior of suppliers and on production costs has become a very important issue. Several restructuring designs for the electric power industry have been proposed. Main goal is to increase the overall market efficiency, trying to study, to develop and to apply different market mechanisms. Auction design is the standard domain for commodity markets. However, properties of different auction mechanism must be studied and determined correctly before their appliance. Generally speaking, different approaches have been proposed in the literature. Game theory analysis has provided an extremely useful methodology to study and derive properties of economic "games", such as auctions. Within this context, an interesting computational approach, for studying market inefficiencies, is the theory of learning in games. This methodology is useful in the context of infinitely repeated games. This paper investigates the nature of the clearing mechanism comparing two different methods, i.e., discriminatory and uniform auctions. The theoretical framework used to perform the analysis is the theory of learning in games. We consider an inelastic demand faced by sellers which use learning algorithms to understand proper strategies for increasing their profits. We model the auction mechanism in two different duopolistic scenario, i.e., a low demand situation, where one seller can clear all the demand, and a high demand condition, where both sellers are requested. Moreover, heterogeneity in the linear cost function is considered. Consistent results are achieved with two different learning algorithmsAgent-based simulation; power-exchange market; market power, reinforcement learning, electricity production costs

Experimental comparison of compulsory and non compulsory arbitration mechanisms

We run a series of experiments to compare the well known arbitration scheme FOA (Final Offer Arbitration) with a new arbitration scheme, non compulsory, we proposed in a companion paper (Tanimura and Thoron (2008)): ROC (Recursive Offer Conciliation). The two mechanisms are also compared with a negotiation without arbitration. We observe that the ROC mechanism seems to cumulate the advantages of the two other procedures, it avoids the high frequency of impasses observed under the FOA procedure and it is as efficient as the Free procedure in this respect. Furthermore, in an asymmetric treatment, it helps the subjects to find an agreement around the equal split of the surplus, like the arbitrator of the FOA procedure does, but without imposing anything on them.negotiation; bargaining; arbitration; Raiffa solution; chilling effect; dispute resolution; experiments

The Italian Electricity Prices in Year 2025: an Agent-Based Simulation

In this paper, we build a realistic large-scale agent-based model of the Italian dayahead-electricity market based on a genetic algorithm and validated over several weeks of 2010, on the basis of exact historical data about supply, demand and network characteristics. A statistical analysis confirms that the simulator well replicates the observed prices. A future scenario for the year 2025 is then simulated, which takes into account market’s evolution and energy vectors’ price dynamics. The future electricity prices are contrasted with the ones that might arise considering also the possible (yet unlikely) construction of new nuclear power (NP) plants. It is shown that future prices will be higher than the actual ones. NP production can reduce the prices and their volatility, but the size of the impact depends on the pattern of the expected demand load, and can be negligible.Electricity market, PUN, Agent-based computational economics, Nuclear power.

The role of information in multi-agent learning

This paper aims to contribute to the study of auction design within the domain of agent-based computational economics. In particular, we investigate the efficiency of different auction mechanisms in a bounded-rationality setting where heterogeneous artificial agents learn to compete for the supply of a homogeneous good. Two different auction mechanisms are compared: the uniform and the discriminatory pricing rules. Demand is considered constant and inelastic to price. Four learning algorithms representing different models of bounded rationality, are considered for modeling agents' learning capabilities. Results are analyzed according to two game-theoretic solution concepts, i.e., Nash equilibria and Pareto optima, and three performance metrics. Different computational experiments have been performed in different game settings, i.e., self-play and mixed-play competition with two, three and four market participants. This methodological approach permits to highlight properties which are invariant to the different market settings considered. The main economic result is that, irrespective of the learning model considered, the discriminatory pricing rule is a more e±cient market mechanism than the uniform one in the two and three players games, whereas identical outcomes are obtained in four players competitions. Important insights are also given for the use of multi-agent learning as a framework for market design.multi-agent learning; auction markets; design economics; agent-based computational economics

Quantum-like models cannot account for the conjunction fallacy

Human agents happen to judge that a conjunction of two terms is more probable than one of the terms, in contradiction with the rules of classical probabilities—this is the conjunction fallacy. One of the most discussed accounts of this fallacy is currently the quantum-like explanation, which relies on models exploiting the mathematics of quantum mechanics. The aim of this paper is to investigate the empirical adequacy of major quantum-like models which represent beliefs with quantum states. We first argue that they can be tested in three different ways, in a question order effect configuration which is different from the traditional conjunction fallacy experiment. We then carry out our proposed experiment, with varied methodologies from experimental economics. The experimental results we get are at odds with the predictions of the quantum-like models. This strongly suggests that this quantum-like account of the conjunction fallacy fails. Future possible research paths are discussed

Experimental comparison of compulsory and non compulsory arbitration mechanisms

We run a series of experiments to compare the well known arbitration scheme FOA (Final Offer Arbitration) with a new arbitration scheme, non compulsory, we proposed in a companion paper (Tanimura and Thoron (2008)): ROC (Recursive Offer Conciliation). The two mechanisms are also compared with a negotiation without arbitration. We observe that the ROC mechanism seems to cumulate the advantages of the two other procedures, it avoids the high frequency of impasses observed under the FOA procedure and it is as efficient as the Free procedure in this respect. Furthermore, in an asymmetric treatment, it helps the subjects to find an agreement around the equal split of the surplus, like the arbitrator of the FOA procedure does, but without imposing anything on them

The role of information in multi-agent learning

This paper aims to contribute to the study of auction design within the domain of agent-based computational economics. In particular, we investigate the efficiency of different auction mechanisms in a bounded-rationality setting where heterogeneous artificial agents learn to compete for the supply of a homogeneous good. Two different auction mechanisms are compared: the uniform and the discriminatory pricing rules. Demand is considered constant and inelastic to price. Four learning algorithms representing different models of bounded rationality, are considered for modeling agents' learning capabilities. Results are analyzed according to two game-theoretic solution concepts, i.e., Nash equilibria and Pareto optima, and three performance metrics. Different computational experiments have been performed in different game settings, i.e., self-play and mixed-play competition with two, three and four market participants. This methodological approach permits to highlight properties which are invariant to the different market settings considered. The main economic result is that, irrespective of the learning model considered, the discriminatory pricing rule is a more e±cient market mechanism than the uniform one in the two and three players games, whereas identical outcomes are obtained in four players competitions. Important insights are also given for the use of multi-agent learning as a framework for market design

An experimental study on learning about voting powers

We investigate experimentally whether subjects can learn, from their limited experiences, about relationships between the distribution of votes in a group and associated voting powers in weighted majority voting systems (WMV). Subjects are asked to play two-stage games repeatedly. In the second stage of the game, a group of four subjects bargains over how to divide fixed amount of resources among themselves through theWMV determined in the first stage. In the first stage, two out of four subjects in the group, independently and simultaneously, choose from two options that jointly determine the distribution of a given number of votes among four members. These two subjects face a 2 × 2 matrix that shows the distribution of votes, but not associated voting powers, among four members for each outcome. Therefore, to obtain higher rewards, subjects need to learn about the latter by actually playing the second stage. The matrix subjects face in the first stage changes during the experiment to test subjects' understanding of relationships between distribution of votes and voting power. The results of our experiments suggest that although (a) many subjects learn to choose, in the votes apportionment stage, the option associated with a higher voting power, (b) it is not easy for them to learn the underlying relationships between the two and correctly anticipate their voting powers when they face a new distribution of votes.experiment, learning, voting power, bargaining

An experimental study on learning about voting powers

We investigate experimentally whether subjects can learn, from their limited experiences, about relationships between the distribution of votes in a group and associated voting powers in weighted majority voting systems (WMV). Subjects are asked to play two-stage games repeatedly. In the second stage of the game, a group of four subjects bargains over how to divide fixed amount of resources among themselves through the WMV determined in the first stage. In the first stage, two out of four subjects in the group, independently and simultaneously, choose from two options that jointly determine the distribution of a given number of votes among four members. These two subjects face a 2 ~ 2 matrix that shows the distribution of votes, but not associated voting powers, among four members for each outcome. Therefore, to obtain higher rewards, subjects need to learn about the latter by actually playing the second stage. The matrix subjects face in the first stage changes during the experiment to test subjects' understanding of relationships between distribution of votes and voting power. The results of our experiments suggest that although (a) many subjects learn to choose, in the votes apportionment stage, the option associated with a higher voting power, (b) it is not easy for them to learn the underlying relationships between the two and correctly anticipate their voting powers when they face a new distribution of votes.

Meaningful Learning in Weighted Voting Games: An Experiment

International audienceBy employing binary committee choice problems, this paper investigates how varying or eliminating feedback about payoffs affects: (1) subjects' learning about the underlying relationship between their nominal voting weights and their expected payoffs in weighted voting games; and (2) the transfer of acquired learning from one committee choice problem to a similar but different problem. In the experiment, subjects choose to join one of two committees (weighted voting games) and obtain a payoff stochastically determined by a voting theory. We found that: (i) subjects learned to choose the committee that generates a higher expected payoff even without feedback about the payoffs they received; and (ii) there was statistically significant evidence of ``meaningful learning'' (transfer of learning) only for the treatment with no payoff-related feedback. This finding calls for re-thinking existing models of learning to incorporate some type of introspection