  Over the past decades, cognitive neuroscientists and behavioral economists
have recognized the value of describing the process of decision making in
detail and modeling the emergence of decisions over time. For example, the time
it takes to decide can reveal more about an agent's true hidden preferences
than only the decision itself. Similarly, data that track the ongoing decision
process such as eye movements or neural recordings contain critical information
that can be exploited, even if no decision is made. Here, we argue that
artificial intelligence (AI) research would benefit from a stronger focus on
insights about how decisions emerge over time and incorporate related process
data to improve AI predictions in general and human-AI interactions in
particular. First, we introduce a highly established computational framework
that assumes decisions to emerge from the noisy accumulation of evidence, and
we present related empirical work in psychology, neuroscience, and economics.
Next, we discuss to what extent current approaches in multi-agent AI do or do
not incorporate process data and models of decision making. Finally, we outline
how a more principled inclusion of the evidence-accumulation framework into the
training and use of AI can help to improve human-AI interactions in the future.
