With an eye towards design of human-in-the-loop systems, we investigate human decision making in a social context for tasks that require the human to make repeated choices among finite alternatives. We consider a human decision maker who receives feedback on his/her own performance as well as on the choices of others performing the same task. We use a drift-diffusion, decision-making model that has been fitted to human neural and behavioral data in sequential, two-alternative, forced-choice tasks and recently extended to the social context with an empirically derived feedback term that depends on choices of other decision makers. We show conditions for this model to be a Markov process, and we derive the steady-state probability distribution for choice sequences and individual performance as a function of the strength of the social feedback. It has recently been shown in behavioral experiments that human decision-making performance for a relatively easy task is decreased with this social feedback; we show that our analytic predictions agree with this finding.