Finitely Repeated Games

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks (2020, methodology): “We evaluate the algorithms in two finitely repeated matrix games [Benoit and Krishna, 1984] and 111 four multi-agent environments within which we define a total of 25 different learning tasks.”
Modeling, Analysis and Control of Networked Evolutionary Games (2015, unknown): “4; (ii) G is Benoit-Krishna Game [4, 28]; (iii) Π = Π − I. Recall that Benoit-Krishna Game has S 0 = {1(D) : Deny, 2(W ) : Waffle, 3(C) : Confess}.”
Participatory Development in the Presence of Endogenous Community Imperfections (2002, background): “Under these circumstances, any early implementation of the approach runs a high risk of causing disillusionment, as well as undue appropriation, by local elites operating within a logic of patronage, of the resources channelled through rural communities in this way.”
Reputation and Reciprocity: Consequences for the Labour Relation (2002, unknown): “Second, even with complete information but with multiple equilibria in the stage game, cooperative equilibria exist in which wages and efforts above w* and e* are observed for all but the last period (Benoit and Krishna (1985)).”