With a principled methodology for systematic design of human–robot decision-making teams as a motivating goal, we seek an analytic, model-based description of the influence of team and network design parameters on decision-making performance. Given that there are few reliably predictive models of human decision making, we consider the relatively well-understood two-alternative choice tasks from cognitive psychology, where individuals make sequential decisions with limited information, and we study a stochastic decision-making model, which has been successfully fitted to human behavioral and neural data for a range of such tasks. We use an extension of the model, fitted to experimental data from groups of humans performing the same task simultaneously and receiving feedback on the choices of others in the group. First, we show how the task and model can be regarded as a Markov process. Then, we derive analytically the steady-state probability distributions for decisions and performance as a function of model and design parameters such as the strength and path of the social feedback. Finally, we discuss application to human–robot team and network design and next steps with a multirobot testbed.

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.

In human-in-the-loop systems, humans are often faced with making repeated choices among finite alternatives in response to observations of the evolving system performance. In order to design humans into such systems, it is important to develop a systematic description of human decision making in this context. We examine a commonly used, drift-diffusion, decision-making model that has been fit to human neural and behavioral data in sequential, two-alternative, forced-choice tasks.

We show how this model and type of task together can be regarded as a Markov process, and we derive the steady-state probability distribution for choice sequences. Using the analytic expression for this distribution, we prove matching behavior for tasks that exhibit a matching point and we compute the sensitivity of steady-state choices to a model parameter that measures the decision maker's "exploratory" tendency.