Special issue: Decision research meets artificial intelligence

The special issue of Decision titled “The Interface Between Artificial Intelligence, Machine Learning and Decision Research” brings together an intellectually diverse collection of work that examines how artificial intelligence (AI) and machine learning (ML) advances are affecting decision research (Davis-Stober et al., 2024).

Articles in the special issue generally address one of two topics. The first topic is how established decision research can be applied to the novel contexts, domains, and choice situations that arise from AI and ML technologies. This includes the nature of understanding online consumer reviews (Alba et al., 2024), recommendation systems (Nobel, 2024), and even the metaverse (Dhami & Zhu, 2024). Going a step further, multiple articles examined interactions between human decision makers and AI technologies (Gopnarayan et al., 2024; Herzog & Franklin, 2024; Meyer, 2024; Oktar et al., 2024). For example, Meyer (2024) demonstrated how decision research can be used to discover and correct various types of algorithmic biases produced by AI.

The second topic involves how AI and ML technologies can be leveraged to develop and test theories of human decision making (Kvam et al., 2024). Multiple articles showcase how advanced AI and ML methodologies can be used to help explain multiattribute choices and risky decision making (Bhui & Dubey, 2024; Ostrovsky & Newell, 2024; Yoo et al., 2024). Other articles focus on how AI and ML can be leveraged to improve the predictability of models of human judgment and decision making (Glöckner et al., 2024; Hoffman, 2024; Huang et al., 2024; Reichman et al., 2024; Shoshan et al., 2024). Simchon and Gilead (2024) offered a different perspective, examining how decision research can be used to advance AI and ML technologies themselves.

It is hardly an exaggeration to say that AI and ML technologies are rapidly changing people’s everyday lives. As digital experiences supplant traditional experiences, the decisions people make and how they make them are becoming increasingly novel, which necessitates that decision researchers study them in novel ways as well. Likewise, AI and ML technologies provide powerful new tools that scientists can harness to investigate and better understand human behavior. Decision scientists need to expand their expertise beyond traditional methods of modeling and incorporate these new technologies into their repertoire of methodology tools for investigating human behavior. That is not to say that these methods are universally superior. Rather, we as decision scientists must critically evaluate the methods’ potential benefits and see how these general principles can inform models and science. Indeed, we ignore them at our own peril.

• Process data, such as eye movements and response time, can be useful for improving AI predictions about human choice.
• Modeling principles from ML, such as regularization and model aggregation, can be useful for improving decision models.
• Human decision making can be improved via interactions with explainable AI—that is, simpler, more transparent models that mimic complex AI technology.
• Divergence between humans and AI in evaluating a decision may stem from a misalignment in how each one represents the objects being considered.

The articles featured in this special issue provide an excellent starting point for decision scientists who are interested in learning more about how AI and ML technologies are affecting the field. These articles provide an important forward-looking perspective on how decision science is evolving in terms of theory development, theory testing, and the myriad new choice paradigms and contexts that humans encounter.

This article is in the Neuroscience and Cognition topic area.

Alba, C., Walasek, L., & Spektor, M. S. (2024). Attention-driven imitation in consumer reviews. Decision, 11(4), 439–449. https://doi.org/10.1037/dec0000238

Bhui, R., & Dubey, R. (2024). Why context should matter. Decision, 11(4), 557–567. https://doi.org/10.1037/dec0000234

Davis-Stober, C. P., Erev, I., & Bhatia, S. (2024). The interface between machine learning, artificial intelligence, and decision research. Decision, 11(4), 435–438. https://doi.org/10.1037/dec0000252

Dhami, M. K., & Zhu, Y. (2024). Possibilities for decision science in the metaverse. Decision, 11(4), 523–536. https://doi.org/10.1037/dec0000235

Glöckner, A., Jekel, M., & Lisovoj, D. (2024). Using machine learning to evaluate and enhance models of probabilistic inference. Decision, 11(4), 633–651. https://doi.org/10.1037/dec0000233

Gopnarayan, M. N., Aru, J., & Gluth, S. (2024). From DDMs to DNNs: Using process data and models of decision making to improve human–AI interactions. Decision, 11(4), 468–480. https://doi.org/10.1037/dec0000239

Herzog, S. M., & Franklin, M. (2024). Boosting human competences with interpretable and explainable artificial intelligence. Decision, 11(4), 493–510. https://doi.org/10.1037/dec0000250

Hoffmann, J. A. (2024). Decisions as ill-posed problems: A scoping review of regularization methods in decision science. Decision, 11(4), 684–699. https://doi.org/10.1037/dec0000248

Huang, S., Golman, R., & Broomell, S. B. (2024). Combining the aggregated forecasts: An efficient method for improving accuracy by stacking multiple weighting models. Decision, 11(4), 668–683. https://doi.org/10.1037/dec0000245

Kvam, P. D., Sokratous, K., Fitch, A., & Hintze, A. (2024). Using artificial intelligence to fit, compare, evaluate, and discover computational models of decision behavior. Decision, 11(4), 599–618. https://doi.org/10.1037/dec0000237

Meyer, J. (2024). Doing artificial intelligence (AI): Algorithmic decision support as a human activity. Decision, 11(4), 481–492. https://doi.org/10.1037/dec0000241

Nobel, N. (2024). Recommender systems: Friend (of choice) or foe? A large-scale field experiment in online shopping platforms. Decision, 11(4), 450–467. https://doi.org/10.1037/dec0000236

Oktar, K., Sucholutsky, I., Lombrozo, T., & Griffiths, T. L. (2024). Dimensions of disagreement: Divergence and misalignment in cognitive science and artificial intelligence. Decision, 11(4), 511–522. https://doi.org/10.1037/dec0000244

Ostrovsky, T., & Newell, B. R. (2024). Verbal reports as data revisited: Using natural language models to validate cognitive models. Decision, 11(4), 568–598. https://doi.org/10.1037/dec0000243

Reichman, D., Peterson, J. C., & Griffiths, T. L. (2024). Machine learning for modeling human decisions. Decision, 11(4), 619–632. https://doi.org/10.1037/dec0000242

Shoshan, V., Hazan, T., & Plonsky, O. (2024). Using machine learning to create an adaptable, scalable, and interpretable behavioral model. Decision, 11(4), 652–667. https://doi.org/10.1037/dec0000247

Simchon, A., & Gilead, M. (2024). A psychologically informed approach to “actuarial” decision making. Decision, 11(4), 700–707. https://doi.org/10.1037/dec0000232

Yoo, J., Chrastil, E. R., & Bornstein, A. M. (2024). Cognitive graphs: Representational substrates for planning. Decision, 11(4), 537–556. https://doi.org/10.1037/dec0000249

Ido Erev, PhD, is a chair professor of data and decision science at the Technion—Israel Institute of Technology. Starting August 2025, he will serve as a professor of psychology and economics at Ohio State University. His research clarifies the conditions under which wise incentive systems can solve behavioral and social problems. Contact Ido Erev.

Sudeep Bhatia, PhD, is an associate professor of psychology at the University of Pennsylvania. He uses computational modeling, behavioral experiments, and large-scale digital data to study how people think and decide. Contact Sudeep Bhatia.

Clintin P. Davis-Stober, PhD, is the Frederick A. Middlebush Professor of Psychological Sciences at the University of Missouri and is the current editor of Journal of Mathematical Psychology. His research interests include decision theory, mathematical modeling, and statistical methods. Contact Clintin Davis-Stober.