Computational Model Library

Lewis' Signaling Chains (version 1.5.0)

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.


Release Notes

This model was described and used to study probe and adjust learning in the following peer-reviewed paper:
Giorgio Gosti (2018) Signalling chains with probe and adjust learning, Connection Science, 30:2, 186-210, DOI: 10.1080/09540091.2017.1345858

Version Submitter First published Last modified Status
1.5.0 Giorgio Gosti Fri Apr 3 15:01:29 2015 Fri Apr 3 15:01:29 2015 Published
1.4.0 Giorgio Gosti Sat Jan 17 15:48:42 2015 Sat Jan 17 15:48:42 2015 Published
1.3.0 Giorgio Gosti Fri Jan 16 14:25:13 2015 Fri Jan 16 14:25:13 2015 Published
1.2.0 Giorgio Gosti Thu Jan 15 14:45:01 2015 Thu Jan 15 14:45:01 2015 Published
1.1.0 Giorgio Gosti Thu Jan 15 14:42:30 2015 Thu Jan 15 14:42:30 2015 Published
1.0.0 Giorgio Gosti Wed Jan 14 14:39:14 2015 Wed Jan 14 14:39:14 2015 Published


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