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Understanding how endosymbiont–host relationships evolve to be mutually beneficial better equips us to predict the future evolutionary course of existing and nascent mutualisms. One mechanism known to influence the evolution of mutualistic endosymbiosis is partner choice, wherein hosts preferentially accept a particular class of partner. However, the extent to which adding partner choice to a system promotes the evolution of mutualism is unknown. In this work, we investigate how partner choice selectivity affects the evolutionary stability and de novo evolution of mutualism. To do so, we implemented tag (i.e. an evolvable label) matching as a mechanism for partner choice in a host–endosymbiont coevolutionary context. We then measured the levels of endosymbiotic mutualism that evolved under a range of partner choice selectivity, tag mutation rates, and endosymbiont vertical transmission rates. Our results demonstrate that tag matching can be effective as an evolvable mechanism for partner choice, due to mutualists and hosts evolving to have more similar tags than parasites and hosts. In addition, we show that partner choice can facilitate the evolution of mutualism, but its specific influence depends on the permissiveness of hosts’ mechanism for partner choice. Specifically, we found ranges of partner selectivity and tag mutation rates that enabled the de novo evolution of exclusive mutualism (i.e. parasites went completely extinct), while other selectivity and mutation rate parameterizations led to coexistence of mutualists and parasites or prevented the evolution and maintenance of mutualisms. These findings pave the way for more precisely engineering systems to promote mutualism. 

In order to make lifelike, versatile learning adaptive in the artificial domain, one needs a very diverse set of behaviors to learn. We propose a parameterized distribution of classic control-style tasks with minimal information shared between tasks. We discuss what makes a task trivial and offer a basic metric, time in convergence, that measures triviality. We then investigate analytic and empirical approaches to generating reward structures for tasks based on their dynamics in order to minimize triviality. Contrary to our expectations, populations evolved on reward structures that incentivized the most stable locations in state space spend the least time in convergence as we have defined it, because of the outsized importance our metric assigns to behavior fine-tuning in these contexts. This work paves the way towards an understanding of which task distributions enable the development of learning.