Agent-Based Models and Human Subject Experiments

This paper considers the relationship between agent-based modeling and economic decision-making experiments with human subjects. Both approaches exploit controlled ``laboratory'' conditions as a means of isolating the sources of aggregate phenomena. Research findings from laboratory studies of human subject behavior have inspired studies using artificial agents in ``computational laboratories'' and vice versa. In certain cases, both methods have been used to examine the same phenomenon. The focus of this paper is on the empirical validity of agent-based modeling approaches in terms of explaining data from human subject experiments. We also point out synergies between the two methodologies that have been exploited as well as promising new possibilities.agent-based models, human subject experiments, zero- intelligence agents, learning, evolutionary algorithms

Walverine: A Walrasian Trading Agent

TAC-02 was the third in a series of Trading Agent Competition events fostering research in automating trading strategies by showcasing alternate approaches in an open-invitation market game. TAC presents a challenging travel-shopping scenario where agents must satisfy client preferences for complementary and substitutable goods by interacting through a variety of market types. Michigan's entry, Walverine, bases its decisions on a competitive (Walrasian) analysis of the TAC travel economy. Using this Walrasian model, we construct a decision-theoretic formulation of the optimal bidding problem, which Walverine solves in each round of bidding for each good. Walverine's optimal bidding approach, as well as several other features of its overall strategy, are potentially applicable in a broad class of trading environments.trading agent, trading competition, tatonnement, competitive equilibrium

meet2trade: An Electronic Market Platform and Experiment System

The development of new electronic markets is challenging, since many factors influence the market outcomes and hence the markets’ success. Even worse, a fundamental lesson learned from economics is that details matter: small changes in market design can have a significant impact on the market participant’s behaviors and thus on the achieved outcomes. Consequently a well structured process for design, implementation, testing and maintenance of markets is required. meet2trade is a software tool suite designed to systematically support each step of such a Market Engineering (ME) process. This paper presents the generic trading platform meet2trade that enables users to individually configure their own electronic markets, to run them on the integrated auction server, and to evaluate them using the built-in full-featured lab experiment system

Call Market Experiments: Efficiency and Price Discovery through Multiple Calls and Emergent Newton Adjustments

We study multiple-unit, laboratory experimental call markets in which orders are cleared by a single price at a scheduled “call”. The markets are independent trading “days” with two calls each day preceded by continuous and public order flow. Markets approach the competitive equilibrium over time. The price formation dynamics operate through the flow of bids and asks configured as the “jaws” of the order book with contract execution structured by an underlying mathematical principle, the Newton method for solving systems of equations. Thus, both excess demand and its slope play a systematic role in call market price discovery

Are agent-based simulations robust? The wholesale electricity trading case

Agent-based computational economics is becoming widely used in practice. This paper explores the consistency of some of its standard techniques. We focus in particular on prevailing wholesale electricity trading simulation methods. We include different supply and demand representations and propose the Experience-Weighted Attractions method to include several behavioural algorithms. We compare the results across assumptions and to economic theory predictions. The match is good under best-response and reinforcement learning but not under fictitious play. The simulations perform well under flat and upward-slopping supply bidding, and also for plausible demand elasticity assumptions. Learning is influenced by the number of bids per plant and the initial conditions. The overall conclusion is that agent-based simulation assumptions are far from innocuous. We link their performance to underlying features, and identify those that are better suited to model wholesale electricity markets.Agent-based computational economics, electricity, market design, experience-weighted attraction (EWA), learning, supply functions, demand aggregation, initial beliefs.

Stochastic Game Theory: Adjustment to Equilibrium Under Noisy Directional Learning

This paper presents a dynamic model in which agents adjust their decisions in the direction of higher payoffs, subject to random error. This process produces a probability distribution of players' decisions whose evolution over time is determined by the Fokker-Planck equation. The dynamic process is stable for all potential games, a class of payoff structures that includes several widely studied games. In equilibrium, the distributions that determine expected payoffs correspond to the distributions that arise from the logit function applied to those expected payoffs. This "logit equilibrium" forms a stochastic generalization of the Nash equilibrium and provides a possible explanation of anomalous laboratory data.bounded rationality, noisy directional learning, Fokker- Planck equation, potential games, logit equilibrium, stochastic potential.

The WALRAS Algorithm: A Convergent Distributed Implementation of General Equilibrium Outcomes

The WALRAS algorithm calculates competitive equilibria via a distributed tatonnement-like process, in which agents submit single-good demand functions to market-clearing auctions. The algorithm is asynchronous and decentralized with respect to both agents and markets, making it suitable for distributed implementation. We present a formal description of this algorithm, and prove that it converges under the standard assumption of gross substitutability. We relate our results to the literature on general equilibrium stability and some more recent work on decentralized algorithms. We present some experimental results as well, particularly for cases where the assumptions required to guarantee convergence do not hold. Finally, we consider some extensions and generalizations to the WALRAS algorithm.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44346/1/10614_2004_Article_137532.pd

Call market experiments : efficiency and price discovery through multiple calls and emergent newton adjustments

We study multiple-unit, laboratory experimental call markets in which orders are cleared by a single price at a scheduled “call”. The markets are independent trading “days” with two calls each day preceded by continuous and public order flow. Markets approach the competitive equilibrium over time. The price formation dynamics operate through the flow of bids and asks configured as the “jaws” of the order book with contract execution featuring elements of an underlying mathematical principle, the Newton-Raphson method for solving systems of equations. Both excess demand and its slope play a systematic role in call market price discovery