  We obtain the conditions for the emergence of the swarm intelligence effect
in an interactive game of restless multi-armed bandit (rMAB). A player competes
with multiple agents. Each bandit has a payoff that changes with a probability
$p_{c}$ per round. The agents and player choose one of three options: (1)
Exploit (a good bandit), (2) Innovate (asocial learning for a good bandit among
$n_{I}$ randomly chosen bandits), and (3) Observe (social learning for a good
bandit). Each agent has two parameters $(c,p_{obs})$ to specify the decision:
(i) $c$, the threshold value for Exploit, and (ii) $p_{obs}$, the probability
for Observe in learning. The parameters $(c,p_{obs})$ are uniformly
distributed. We determine the optimal strategies for the player using complete
knowledge about the rMAB. We show whether or not social or asocial learning is
more optimal in the $(p_{c},n_{I})$ space and define the swarm intelligence
effect. We conduct a laboratory experiment (67 subjects) and observe the swarm
intelligence effect only if $(p_{c},n_{I})$ are chosen so that social learning
is far more optimal than asocial learning.
