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Over the past decades, cognitive neuroscientists and behavioral economistshave recognized the value of describing the process of decision making indetail and modeling the emergence of decisions over time. For example, the timeit takes to decide can reveal more about an agent's true hidden preferencesthan only the decision itself. Similarly, data that track the ongoing decisionprocess such as eye movements or neural recordings contain critical informationthat can be exploited, even if no decision is made. Here, we argue thatartificial intelligence (AI) research would benefit from a stronger focus oninsights about how decisions emerge over time and incorporate related processdata to improve AI predictions in general and human-AI interactions inparticular. First, we introduce a highly established computational frameworkthat assumes decisions to emerge from the noisy accumulation of evidence, andwe present related empirical work in psychology, neuroscience, and economics.Next, we discuss to what extent current approaches in multi-agent AI do or donot incorporate process data and models of decision making. Finally, we outlinehow a more principled inclusion of the evidence-accumulation framework into thetraining and use of AI can help to improve human-AI interactions in the future.

From DDMs to DNNs: Using process data and models of decision-making to improve human-AI interactions