Convergence in Human Decision-Making Dynamics
Ming Cao, Andrew Stewart and Naomi Ehrich Leonard
Systems and Control Letters, Volume 59, pp. 87-97, 2010.
A class of binary decision-making tasks called the two-alternative
forced-choice task has been used extensively in psychology and
behavioral economics experiments to investigate human
decision making. The human subject makes a choice between two
options at regular time intervals and receives a reward after each
choice; for a variety of reward structures, these experiments show
convergence of the aggregate behavior to rewards that are often
suboptimal. In this paper we present two models of human
decision making: one is the Win-Stay, Lose-Switch (WSLS) model and the
other is a deterministic limit of the popular Drift Diffusion (DD) model.
With these models we prove convergence of the human behavior to the
observed aggregate decision making for reward structures with
matching points. The analysis is motivated by human-in-the-loop systems, where humans are often
required to make repeated choices among finite alternatives in response to evolving system
performance measures. We discuss application of the convergence result to design of
human-in-the-loop systems using a map from the human subject to a human supervisor.
(2.3 MB pdf)
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