Gale-Shapley Matching in an Evolutionary Trade Network Game

This study investigates the performance of Gale-Shapley matching in an evolutionary market context. Computational experimental findings are reported for an evolutionary match-and-play trade network game in which resource constrained traders repeatedly choose and refuse trade partners in accordance with Gale-Shapley matching, participate in risky trades modeled as two-person prisoner's dilemma games, and evolve their trade behavior over time. Particular attention is focused on correlations between ex ante market structure and the formation of trade networks, and between trade network formation and the types of trade behavior and social welfare outcomes that these trade networks support. Related work can be accessed here: http://www.econ.iastate.edu/tesfatsi/tnghome.htmGale-Shapley matching; partner choice; agent-based modeling; evolutionary market game; Trade Network Game (TNG)

Dynamic Testing of Wholesale Power Market Designs: An Open-Source Agent-Based Framework

In April 2003 the U.S. Federal Energy Regulatory Commission proposed a complicated market design - the Wholesale Power Market Platform (WPMP) ï¾– for common adoption by all U.S. wholesale power markets. Versions of the WPMP have been implemented in New England, New York, the mid-Atlantic states, the Midwest, and the Southwest, and California. Strong opposition to the WPMP persists among some industry stakeholders, however, due largely to a perceived lack of adequate performance testing. This study reports on the model development and open-source implementation (in Java) of a computational wholesale power market organized in accordance with core WPMP features and operating over a realistically rendered transmission grid subject to congestion effects. The traders within this market model are strategic profit-seeking agents whose learning behaviors are based on data from human-subject experiments. Our key experimental focus is the complex interplay among structural conditions, market protocols, and learning behaviors in relation to short-term and longer-term market performance. Findings for a dynamic 5-node transmission grid test case are presented for concrete illustration. Annotated pointers to related work can be accessed here: http://www.econ.iastate.edu/tesfatsi/AMESMarketHome.htm

Structure, Behavior, and Market Power in an Evolutionary Labor Market with Adaptive Search

This study uses an agent-based computational labor market framework to experimentally study the relationship between job capacity, job concentration, and market power. Job capacity is measured by the ratio of potential job openings to potential work orders, and job concentration is measured by the ratio of work suppliers to employers. For each experimental treatment, work suppliers and employers repeatedly seek preferred worksite partners based on continually updated expected utility, engage in efficiency-wage worksite interactions modelled as prisoner's dilemma games, and evolve their worksite behaviors over time. The main finding is that job capacity consistently trumps job concentration when it comes to predicting the relative ability of work suppliers and employers to exercise market power. Related work can be accessed here: http://www.econ.iastate.edu/tesfatsi/tnghome.htmmarket power; agent-based computational economics; evolutionary game; Labor market dynamics; job capacity; job concentration; adaptive search; networks; endogenous interactions

Market Power and Efficiency in a Computational Electricity Market with Discriminatory Double-Auction Pricing

This study reports experimental market power and efficiency outcomes for a computational wholesale electricity market operating in the short run under systematically varied concentration and capacity conditions. The pricing of electricity is determined by means of a clearinghouse double auction with discriminatory midpoint pricing. Buyers and sellers use a modifed Roth-Erev individual reinforcement learning algorithm to determine their price and quantity offers in each auction round. It is shown that high market efficiency is generally attained, and that market microstructure is strongly predictive for the relative market power of buyers and sellers independently of the values set for the reinforcement learning parameters. Results are briefly compared against results from an earlier electricity study in which buyers and sellers instead engage in social mimicry learning via genetic algorithms. Related work can be accessed at: http://www.econ.iastate.edu/tesfatsi/AMESMarketHome.htmagent-based computational economics; Wholesale electricity market; restructuring; repeated double auction; market power; efficiency; concentration; capacity; individual reinforcement learning; genetic algorithm social learning

Separation and Volatility of Locational Marginal Prices in Restructured Wholesale Power Markets

This study uses an agent-based test bed ("AMES") to investigate separation and volatility of locational marginal prices (LMPs) in an ISO-managed restructured wholesale power market operating over an AC transmission grid. Particular attention is focused on the dynamic and cross-sectional response of LMPs to systematic changes in demand-bid price sensitivities and supply-offer price cap levels under varied learning specifications for the generation companies. Also explored is the extent to which the supply offers of the marginal (price-determining) generation companies induce correlations among neighboring LMPs. Related work can be accessed at: http://www.econ.iastate.edu/tesfatsi/AMESMarketHome.htmRestructured wholesale power markets; multi-agent learning; demand-bid price sensitivity; AMES Wholesale Power Market Test Bed; agent-based modeling; locational marginal prices (LMPs); LMP separation; LMP volatility; supply-offer price caps

Nonlocal Automated Comparative Static Analysis

This paper reviews work on the development of a program Nasa for the automated comparative static analysis of parameterized nonlinear systems over parameter intervals. Nasa incorporates a fast and efficient algorithm Feed for the automatic evaluation of higher-order partial derivatives, as well as an adaptive homotopy continuation algorithm for obtaining all required initial conditions. Applications are envisioned for fields such as economics where models tend to be complex and closed-form solutions are difficult to obtain

Validation of Agent-Based Models in Economics and Finance

Since the survey by Windrum et al. (Journal of Artificial Societies and Social Simulation 10:8, 2007), research on empirical validation of agent-based models in economics has made substantial advances, thanks to a constant flow of high-quality contributions. This Chapter attempts to take stock of such recent literature to offer an updated critical review of the existing validation techniques. We sketch a simple theoretical framework that conceptualizes existing validation approaches, which we examine along three different dimensions: (i) comparison between artificial and real-world data; (ii) calibration and estimation of model parameters; and (iii) parameter space exploration. Finally, we discuss open issues in the field of ABM validation and estimation. In particular, we argue that more research efforts should be devoted toward advancing hypothesis testing in ABM, with specific emphasis on model stationarity and ergodicity

Market design test environments

Power industry restructuring continues to evolve at multiple levels of system operations. At the bulk electricity level, several organizations charged with regional system operation are implementing versions of a wholesale power market platform (WPMP) in response to U.S. Federal Energy Regulatory Commission initiatives. Recently the Energy Policy Act of 2005 and several regional initiatives have been pressing the integration of demand response as a resource for system operations. These policy and regulatory pressures are driving the exploration of new market designs at the wholesale and retail levels. The complex interplay among structural conditions, market protocols, and learning behaviors in relation to short-term and longer-term market performance demand a flexible computational environment where designs can be tested and sensitivities to power system and market rule changes can be explored. This paper discusses the use of agent-based computational methods for the study of electricity markets at the wholesale and retail levels, and explores distinctions in problem formulation between these levels© 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. DOI: 10.1109/PES.2006.1708927</p