Monomorphic evolutionarily stable ecological equilibrium |
A combination of two models: evolutionary game theory and Lotka–Volterra model of population ecology, which assumes all individuals in each species behave identically. |
Introduction of the evolutionary ecology stability concept, with Maynard Smith's original definition of an evolutionarily stable strategy for a single species as a special case. |
(Cressman & Garay, 2003) |
Complex interactions with incomplete information |
A theoretical analysis of the interactions of partially cooperative agents in situations with incomplete information, focusing on the case of two types of agents, each with two strategies. |
Agents can find suitable strategies through evolution and adaptation and for two or more strategies can find a steady state. |
(Sim & Wang, 2005) |
Scenario calculus |
Introduces a new method for analyzing features of complex systems with emergent behaviors, looking at convergence, and other agent parameters. |
The experiments uncovered dynamic features relating to convergence that were very difficult to obtain by formal logic. |
(Wang & Zhu, 2007) |
Complex coevolutionary dynamics |
An investigation of how theoretical analyses of infinite‐sized coevolving populations may not apply to more realistic finite‐sized populations. |
Infinite population simulations do not always represent real trajectories and are not always representative of co‐evolutionary dynamics of large and finite populations. |
(Tino et al., 2013) |
Coevolution and games |
Explores cycling behaviors in the context of coevolving game‐playing individuals, using the game Othello. |
The method was able to find strong value functions in an experiment evolving weighted piece counter value functions to play the Othello board game. |
(Samothrakis et al., 2013) |
ABM parameter search with complexification |
Studies complexification approaches with evolution to aid evolution in finding parameter values for agent‐based Boid and Bee models automatically. |
Both models benefited from the use of complexification, which improves evolutionary algorithms when the use of genomes with variable length and complexity is possible. |
(Wagner et al., 2015) |