Bargaining Foundations for the Outside Option Principle

Abstract We study a bargaining game in which a seller can trade with one of two buyers, who have values h and l (h>l). The outside option principle (OOP) predicts that, as players become patient, the seller trades with the high-value buyer with probability converging to 1 at a price converging to max(h/2,l). While this prediction is supported by the Markov perfect equilibrium (MPE), a wide range of trading outcomes may emerge in subgame perfect equilibria (SPEs): in the patient limit, the seller can obtain any price in the interval [h/2,h] (and no other); moreover, allocative inefficiency and costly delay are possible. We propose equilibrium refinements less restrictive than Markov behavior that guarantee trading outcomes consistent with the OOP. One refinement requires that a buyer’s relative probability of trade does not increase dramatically following a failed negotiation with that buyer. Another refinement posits that the seller does not approach a buyer hoping that negotiations fail. SPEs satisfying both refinements conform with the OOP (but need not be MPEs). Our benchmark model features strategic matching by the seller. We provide a parallel analysis for the random matching protocol. Under random matching, prices in SPEs may also rise above and fall below l, but have a narrower range. A refinement particular to this protocol that restores the OOP requires that a random mismatch should not impact the seller excessively.

Authors

Institutions

Publication Details

Journal
The Review of Economic Studies
Published
2025-06-28
DOI
https://doi.org/10.1093/restud/rdaf053
Citations
1
Primary Topic
Economic Policies and Impacts
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Bargaining Foundations for the Outside Option Principle

Dilip Abreu, Mihai Manea
1 citations
The Review of Economic Studies
Economic Policies and Impacts
article

Bargaining Foundations for the Outside Option Principle

Dilip Abreu, Mihai Manea
article en
1 citations

Abstract

Abstract We study a bargaining game in which a seller can trade with one of two buyers, who have values h and l (h>l). The outside option principle (OOP) predicts that, as players become patient, the seller trades with the high-value buyer with probability converging to 1 at a price converging to max(h/2,l). While this prediction is supported by the Markov perfect equilibrium (MPE), a wide range of trading outcomes may emerge in subgame perfect equilibria (SPEs): in the patient limit, the seller can obtain any price in the interval [h/2,h] (and no other); moreover, allocative inefficiency and costly delay are possible. We propose equilibrium refinements less restrictive than Markov behavior that guarantee trading outcomes consistent with the OOP. One refinement requires that a buyer’s relative probability of trade does not increase dramatically following a failed negotiation with that buyer. Another refinement posits that the seller does not approach a buyer hoping that negotiations fail. SPEs satisfying both refinements conform with the OOP (but need not be MPEs). Our benchmark model features strategic matching by the seller. We provide a parallel analysis for the random matching protocol. Under random matching, prices in SPEs may also rise above and fall below l, but have a narrower range. A refinement particular to this protocol that restores the OOP requires that a random mismatch should not impact the seller excessively.

The Review of Economic StudiesVol. 93(2)
New York University (US), Stony Brook University (US)
Openalex Percentile: Top 77%
Economic Policies and Impacts
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.