Price Formation in Double Auctions

By Steven Gjerstad, J. Dickhaut. E-Commerce Agents, 2001

474 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Marketing, Safety Research

View Paper: https://www.semanticscholar.org/paper/d047e65f1d773fef2d1dbf43b5f9cb99601af1d5

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Citations in the corpus

Agent-Based Computational Economics: Growing Economies From the Bottom Up (2002, methodology): “Additional types of learning algorithms that have been used include action-based reinforcement learning algorithms [33, 88], Q-learning [92, 104], classifier systems [41], and various forms of learning algorithms that have been adapted for use in automated markets [38, 91].”
Internet-Based Virtual Stock Markets for Business Forecasting (2003, background): “A double auction directly matches the purchase and sale orders of participants Management Science/Vol. 49, No. 10, October 2003 1317 (Gjerstad and Dickhaut 1998).”
Agent-Based Models and Human Subject Experiments (2004, unknown): “16 Gjerstad and Dickhaut (1998) provide a particularly elegant characterization of the DA and propose heuristic rules by which buyers and sellers assess and update the probability that their bids or asks will be accepted, given market history.”
Learning the IPA market with individual and social rewards (2007, unknown): “Gjer-stad and Dickhaut [15] apply a similar approach but for single Double Auctions.”
Local Energy Markets: Paving the Path Toward Fully Transactive Energy Systems (2019, methodology): “In addition, our method may be taken further by learning market and opponent parameters from experience [23], thus reducing the need for prior knowledge and broadening the scope of applicability into more realistic scenarios where agents might change behaviours [24].”
Choosing Samples to Compute Heuristic-Strategy Nash Equilibrium (2003, background): “This has led a number of researchers to devise innovative heuristic strategies—typically employing economic reasoning, opti mization, and artificial intelligence techniques—to complex games including the Trading Agent Competitions [6, 20, 23], market-based scheduling [17], and the continuous d ble a”
Analyzing Complex Strategic Interactions in Multi-Agent Systems (2002, methodology): “Models of CDAs have been extensively studied using both human traders (Smith 1982) and computerized traders (Rust, Miller, & Palmer 1993; Cliff & Bruten 1997; Gjerstad & Dickhaut 1998).”