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.

P. Thus, the workshops and survey described in the paper were used to gather the data and complete the project. For the purpose of this paper, we performed Stage 1 (concepts outline) to reuse the original items that we found relevant to the open strategy scale. In Stage 2, we designed the study to test the scale, and for this purpose, we used the recalled raw data. The authors would like to express their apologies for any inconvenience that may have arisen.

mode preferences (Siedlok and Hibbert 2014). The academic context in which the researcher is embedded is often a key influencer. In recent years, universities and institutions have embraced interdisciplinarity through research centers-which often have members from multiple disciplines, funded projects and through higher education instructional programs (Leahey and Barringer 2020). Yet, there is more to be done to leverage what interdisciplinary research has to offer. As Karl Popper famously observed: 'We are not students of some subject matter, but students of problems. And problems may cut right across the boundaries of any subject matter or discipline.' (Popper 2002, pg. 88).

the error bars were defined incorrectly in the notes for Figures 2 and 3 . The notes should have stated, "Error bars represent fixed effects regression estimates +/− standard errors." All versions of this article have been corrected.

Individuals often make decisions in a social environment where social influence can impact on people’s decision-making domains such as online purchasing, political voting and voluntary vaccine uptake. Social influence can be recognised as the intentional or unintentional change in an individual’s belief, perception, or behaviours caused by an information diffusion process embedded in a social network. However, there is limited research on how this diffusion process is shaped by the topology or structure of the social network. This work provides an exploratory and systematic analysis of how decision-making outcomes in a population can be affected by both the structure of the social network and the starting node of where new information starts to diffuse. Simulation results considering three common network structures highlight how social networks with clear community structures lead to a larger absolute impact on decision-making outcomes and networks where the social connections follow a preferential attachment rule show the largest relative impact than the others. The results also suggest scenarios in which introducing new pieces of information to the social network can facilitate the information diffusion process and produce a more significant impact in terms of the overall population decision-making process.