To investigate the influence of information about fellow group members in a constrained decision-making context, we develop four two-armed bandit tasks in which subjects freely select one of two options ($A$or$B$) and are informed of the resulting reward following each choice. Rewards are determined by the fraction$x$of past$A$choices by two functions$f_{A}(x),f_{B}(x)$(unknown to the subject) which intersect at a matching point$\bar{x}$that does not generally represent globally optimal behavior. Playing individually, subjects typically remain close to the matching point, although some discover the optimum. Each task is designed to probe a different type of behavior, and subjects work in parallel in groups of five with feedback of other group members' choices, of their rewards, of both, or with no knowledge of others' behavior. We employ a soft-max choice model that emerges from a drift-diffusion process, commonly used to model perceptual decision making with noisy stimuli. Here the stimuli are replaced by estimates of expected rewards produced by a temporal-difference reinforcement-learning algorithm, augmented to include appropriate feedback terms. Models are fitted for each task and feedback condition, and we compare choice allocations averaged across subjects and individual choice sequences to highlight differences between tasks and intersubject differences. The most complex model, involving both choice and reward feedback, contains only four parameters, but nonetheless reveals significant differences in individual strategies. Strikingly, we find that rewards feedback can be either detrimental or advantageous to performance, depending upon the task.

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.

The focus of the work in this paper is a systems-theoretic construction, analysis, and evaluation of a deterministic model of human decision making relative to experimental data. In sequential two-alternative forced choice decision tasks, a human subject is presented with two choices at every time step, is given finite time to select one of the choices, and receives a reward after a choice is made (presented as a number on a computer screen). The goal for the human is to obtain the maximal reward while not knowing the underlying reward assignment process. In this work, we present a parameterized deterministic model for human decision making in this context and analyze optimality and stability using a finite state machine approach. This model is then evaluated relative to experimental data from human subjects performing each of six tasks.