Scott Page, Jameson Toole
Paper #: 10-09-020
We explore the ability of a locally informed individual agent to predict the future state of an automaton in systems of varying degrees of complexity using Wolfram's one-dimensional binary cellular automata. We then compare the agent's performance to that of two small groups of agents voting by majority rule. We find stable rules (Class I) to be highly predictable, and most complex (Class IV) and chaotic rules (Class III) to be unpredictable. However, we find rules that produce regular patterns (Class II) vary widely in their predictability. We then show that the predictability of a Class II rule depends on whether the rules produces vertical or horizontal patterns. We comment on the implications of our findings for the limitations of collective wisdom in complex environments.