A comparison of zero- and minimal-intelligence agendas in majority-rule voting models

Abstract Emergent behavior in repeated collective decisions of minimally intelligent agents—who at each step time invoke majority rule to choose between a status quo and random challenge—can manifest through the long-term stationary probability distributions Markov chain. We use this known technique compare two kinds voting agendas: zero-intelligence agenda that chooses challenger uniformly from union set winning challengers. Google Co-Lab’s GPU accelerated computing environment compute for some simple examples spatial-voting budget-allocation scenarios. find model using converges more slowly, but cases better outcomes.