  Evidential cooperation in large worlds (ECL) refers to the idea that humans
and other agents can benefit by cooperating with similar agents with differing
values in causally disconnected parts of a large universe. Cooperating provides
agents with evidence that other similar agents are likely to cooperate too,
resulting in gains from trade for all. This could be a crucial consideration
for altruists.
  I develop a game-theoretic model of ECL as an incomplete information
bargaining problem. The model incorporates uncertainty about others' value
systems and empirical situations, and addresses the problem of selecting a
compromise outcome. Using the model, I investigate issues with ECL and outline
open technical and philosophical questions.
  I show that all cooperators must maximize the same weighted sum of utility
functions to reach a Pareto optimal outcome. However, I argue against selecting
a compromise outcome implicitly by normalizing utility functions. I review
bargaining theory and argue that the Nash bargaining solution could be a
relevant Schelling point. I introduce dependency equilibria (Spohn 2007), an
equilibrium concept suitable for ECL, and generalize a folk theorem showing
that the Nash bargaining solution is a dependency equilibrium. I discuss gains
from trade given uncertain beliefs about other agents and analyze how these
gains decrease in several toy examples as the belief in another agent
decreases.
  Finally, I discuss open issues in my model. First, the Nash bargaining
solution is sometimes not coalitionally stable, meaning that a subset of
cooperators can unilaterally improve payoffs by deviating from the compromise.
I investigate conditions under which stable payoff vectors exist. Second, I
discuss how to model agents' default actions without ECL.
