Various potential strategic interactions between a “strong” Artificial intelligence and humans are analyzed using simple 2 × 2 order games, drawing on the New Periodic Table of those games developed by Robinson and Goforth. Strong risk aversion on the part of the human player leads to shutting down the AI research program, but alternative preference orderings by the human and the AI result in Nash equilibria with interesting properties. Some of the AI-Human games have multiple equilibria, and in other cases (...) Pareto-improvement over the Nash equilibrium could be attained if the AI’s behavior towards humans could be guaranteed to be benign. The preferences of a superintelligent AI cannot be known in advance, but speculation is possible as to its ranking of alternative states of the world, and how it might assimilate the accumulated wisdom of humanity. (shrink)

An arms race for an artificial general intelligence would be detrimental for and even pose an existential threat to humanity if it results in an unfriendly AGI. In this paper, an all-pay contest model is developed to derive implications for public policy to avoid such an outcome. It is established that, in a winner-takes-all race, where players must invest in R&D, only the most competitive teams will participate. Thus, given the difficulty of AGI, the number of competing teams is unlikely (...) ever to be very large. It is also established that the intention of teams competing in an AGI race, as well as the possibility of an intermediate outcome, is important. The possibility of an intermediate prize will raise the probability of finding the dominant AGI application and, hence, will make public control more urgent. It is recommended that the danger of an unfriendly AGI can be reduced by taxing AI and using public procurement. This would reduce the pay-off of contestants, raise the amount of R&D needed to compete, and coordinate and incentivize co-operation. This will help to alleviate the control and political problems in AI. Future research is needed to elaborate the design of systems of public procurement of AI innovation and for appropriately adjusting the legal frameworks underpinning high-tech innovation, in particular dealing with patenting by AI. (shrink)