Using experiments to compare the predictive power of models of multilateral negotiations
Cary Deck () and
Charles Thomas
International Journal of Industrial Organization, 2020, vol. 70, issue C
Abstract:
We conduct unstructured negotiations in a laboratory experiment designed to empirically assess the predictive power of models of the multilateral negotiations observed in diverse strategic settings. For concreteness we consider two sellers negotiating with a buyer who wants to make only one trade, and we categorize the models by whether introducing a second seller to bilateral negotiations always, never, or sometimes increases the buyer's payoff. Our experiment features two scenarios within which the three categories of models have observationally distinct predictions: a differentiated scenario with one high-surplus seller and one low-surplus seller, and a homogeneous scenario with identical high-surplus sellers. In both scenarios the buyer tends to trade with a high-surplus seller at terms indistinguishable from those in bilateral negotiations with a high-surplus seller, meaning that introducing a competing seller does not substantially affect the observed terms of trade. Our findings match the predictions from models in the never-matters category, supporting their use when modeling multilateral negotiations.
Keywords: Negotiations & Bargaining; Laboratory Experiments; Procurement; Mergers & Acquisitions; Investment (search for similar items in EconPapers)
JEL-codes: C7 C9 D4 L1 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167718720300345
Full text for ScienceDirect subscribers only
Related works:
Working Paper: Using Experiments to Compare the Predictive Power of Models of Multilateral Negotiations (2016) 
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:indorg:v:70:y:2020:i:c:s0167718720300345
DOI: 10.1016/j.ijindorg.2020.102612
Access Statistics for this article
International Journal of Industrial Organization is currently edited by P. Bajari, B. Caillaud and N. Gandal
More articles in International Journal of Industrial Organization from Elsevier
Bibliographic data for series maintained by Catherine Liu ().