Working Paper: CEPR ID: DP6424
Authors: Carlo Carraro; Alessandra Sgobbi
Abstract: The relevance of bargaining to everyday life can easily be ascertained, yet the study of any bargaining process is extremely hard, involving a multiplicity of questions and complex issues. The objective of this paper is to provide new insights on some dimensions of the bargaining process ? asymmetries and uncertainties in particular ? by using a non-cooperative game theory approach. We develop a computational model which simulates the process of negotiation among more than two players, who bargain over the sharing of more than one pie. Through numerically simulating several multiple issues negotiation games among multiple players, we identify the main features of players? optimal strategies and equilibrium agreements. As in most economic situations, uncertainty crucially affects also bargaining processes. Therefore, in our analysis, we introduce uncertainty over the size of the pies to be shared and assess the impacts on players? strategic behaviour. Our results confirm that uncertainty crucially affects players? behaviour and modify the likelihood of a self-enforcing agreement to emerge. The model proposed here can have several applications, in particular in the field of natural resource management, where conflicts over how to share a resource of a finite size are increasing.
Keywords: bargaining; noncooperative game theory; simulation models; uncertainty
JEL Codes: C61; C71; C78
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
Cause | Effect |
---|---|
uncertainty over the size of the pies (D80) | players' strategic behavior (C72) |
uncertainty over the size of the pies (D80) | likelihood of reaching a self-enforcing agreement (D74) |
uncertainty increases (D89) | negotiation process takes longer (C78) |
uncertainty leads to players proposing incompatible values (D89) | offers do not converge to a feasible solution (C62) |
introduction of uncertainty (D89) | players bargain harder (C78) |
uncertainty (D89) | players might be better off in stochastic scenarios (C73) |