TABLE 1.

Theoretical ecosystems

Name/focus	Description	Results/findings	References
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)