game theory? decision tree. very small environment. at least two agents. a decision tree is analogous to reasons engaged in either diplomacy or trade. interested in diplomacy and cooperation. assuming all possibilities are already discovered, an environment with multiple agents finds greatest success if the agents work together rather than expending energy in competition. what is very simple environment, and then how can we reason around strategies i; that environment? to facilitate this we pick actions with reasons. ummm thinking of variable A and B. say raising or lowering A by B is an action (add, sub). if a goal is to mutate A, then mutating B becomes a means for that end. maybe four actions; add/sub A with B to A, and inc/dec B. we can then try to consider a decision structure around this. a naive decision structure considers fully described world states after events. the world is A and B. consider judgement metric ie reward related to distance of A from a target Z. if B is fixed, A+B or A-B mutate the metric immediately. If we then consider inc/dec of B, this mutates the metric in a delayed manner. The naive tree must be explored in some depth to see that there is return from doing this. This is seen easier of the add/sub operation is limited. A complex system could form an analysis of the two categories of operation, and describe the situations in which one outcompetes the other. Regardless, a reason structure is formed: something that (a) validates a choice to either add/sub or inc/dec to meet Z and (b) holds that inc/dec is being done for the plan to add/sub, and that add/sub is done for the plan to have A=Z . It is this second structure that is of interest — the trees of reasons. this structure is a complex mutation of the naive tree of world events. they relate to the same data ie what can happen in the world, based on what the agent chooses to do, for the purpose of selecting the actions that are best for the agent. … please continue monologueing since this topic is so severely inhibited … i flubbed and am having big inhib. so what’s of interest is how the reason trees can be used in a shared way for the agents to not only predict the actions of each other but form wiser reason trees that include the reasons of others, and … long story short to evolve communication, community, collective decision making structures and/or governments, and even things analogous to families and caring behavior. all analogies, obviously, not literal, but so important for protecting life from harm of automated borg. they form cooperative structures even if they are just arithmetic agents. they have to figure out how to work together to meet their goals most effectively. assuming they have solved the environment independently already. and it’s our responsibility to figure out how.