In organizations or firms, and in multicellular systems, human or cellular organisms require complex signaling networks to coordinate their effort and improve their odds. Lewis’ signaling games are extensive form games originally derived to describe the evolution of the association between a sign and its meaning. Signaling chains generalize Lewis’ signaling games in order to model the evolution of signaling in complex systems. More precisely this model empirically evaluates how effective the probe and adjust learning dynamics is in evolving signaling conventions on signaling chains. In signaling chains, there are four fundamental elements: a sender, a receiver, a transmitter, and a state of Nature, which provides random events that are independent of the players behavior. At each time t, Nature chooses its state with some probability, the sender observes Nature’s state, and sends a signal through a chain of transmitters to the receiver. The receiver does not know the state of Nature, and she must chose an action. Finally, the receiver’s action and Nature’s state determine the sender’s and receiver’s payoff. In this model, cases in which the sender and receiver share a perfect common interest are considered. If the act matches Nature’s state, the sender and the receiver get a payoff of one, otherwise they get a payoff of zero.
This is a companion discussion topic for the original entry at https://www.comses.net/codebases/4493/releases/1.3.0/