Mindshaping and strategic learning

Mindshaping clearly often has a strategic aspect. Game theory is the established modelling technology for representing and analysing strategy. Therefore, there is motivation for applying it to mindshaping. But this presents challenges. In the standard mathematics of game theory, the preferences of players must be fixed in advance and cannot change during a game. But mindshaping is fundamentally about people (or other agents) influencing one another's preferences. A related challenge is that in standard games on networks, influence propagates in only one direction. But mindshaping processes often involve mutual influence. In this chapter, we show how Conditional Game Theory, originally developed for modelling distributed control in robots but recently extended to apply to social relationships, provides a mathematical model of strategic preference change. We also show how a Markov-chain convergence theorem, newly applied to social networks, allows mutual influence to be mathematically represented. We discuss some general implications of applying these mathematics to mindshaping. One significant upshot for philosophy of social science is that we integrate sociological and economic theory with respect to modelling social change. A second implication is that we provide a basis for designing laboratory experiments aimed at identifying mindshaping behaviour and quantitatively measuring its effects.