Cleotilde Gonzalez
Professor of Social & Decision Sciences Carnegie Mellon University
- Pittsburgh PA
Cleotilde Gonzalez studies human decision making in dynamic environments and how humans interact with technology.
Biography
Coty is a 2024 AAAS Fellow and the 15th faculty member from Dietrich College of Humanities and Social Sciences to be elected a fellow of AAAS. She is a lifetime fellow of the Cognitive Science Society and of the Human Factors and Ergonomics Society. She is a Senior Editor for Topics in Cognitive Science, a Consulting Editor for Decision, and Associate Editor for the System Dynamics Review and a member of editorial boards in multiple other journals including: Cognitive Science, Psychological Review, Perspectives on Psychological Science, and others.
Areas of Expertise
Media Appearances
The Next Generation of AI Won’t Replace Humans – It Will Work With Them
CMU Dietrich College of Humanities and Social Sciences online
2026-06-29
Cleotilde “Coty” Gonzalez has a different vision for the future – one in which humans and AI work together as teammates. In fact, she and a cohort of collaborators have recently published three scientific papers (see sidebar) investigating the facets of “cognitive AI” and how it could partner with humans on teams.
Inside the High-Stakes Decisions of the NFL Draft
CMU Dietrich College of Humanities and Social Sciences online
2026-05-31
By nature, the draft is about planning for the future. Teams won’t know the results of their picks for months or years to come. That kind of planning is not something people are generally good at, said Cleotilde (Coty) Gonzalez, a professor in CMU’s Department of Social and Decision Sciences.
Collective intelligence framework shows how human-AI teams may make better decisions
Tech Xplore online
2026-04-30
"AI is becoming deeply embedded in collective decision making, marking a profound transformation in how decisions are made across domains, from health care and emergency response to finance, transportation, and governance," explains Cleotilde Gonzalez, Professor of Cognitive Decision Science at CMU, and lead author on the paper.
Dating choices or draft picks — the cognitive science behind either is similar, CMU professors say
TribLIVE online
2026-04-10
“You date someone and you make a choice to commit to this person, or keep looking,” said Cleotilde Gonzalez, a cognitive decision science professor at Carnegie Mellon University. “That is precisely the exploration–exploitation dilemma. The problem is we don’t know when to stop.”
Media
Social
Industry Expertise
Accomplishments
Research
Her work includes the development of a theory of decisions from experience called Instance-Based Learning Theory (IBLT). IBL models are used to explain human behavior and to predict human choice. They have been integrated in multiple practical domains including: cybersecurity, network science, human-machine teaming, and others.
Publications
Coty has published hundreds of papers in journals and peer-reviewed proceedings involving a diverse set of fields deriving from her contributions to Cognitive Science.
Partnerships
Coty has been Principal or Co-Investigator on a wide range of multi-million and multi-year collaborative efforts with government and industry, including current efforts on NSF AI National Institutes, Multi-University Research Initiative grants from the Army Research Laboratories and Army Research Office; large collaborative projects with the Defense Advanced Research Projects Agency (DARPA) and the Intelligence Advanced Research Projects Activity (IARPA).
Education
Texas Tech University
Ph.D
Information Systems/Human Factors
1996
Texas Tech University
M.Sc.
Management Information Systems
1992
Universidad de las Américas Puebla
MBA
1990
Universidad de las Américas Puebla
B.Sc.
Computer Science
1986
Affiliations
- AI National Institute for Societal Decision Making, AISDM : Co-Director
- American Association for the Advancement of Science (AAAS) : Fellow
- Cognitive Science Society : Lifetime Fellow
- Human Factors and Ergonomics Society : Lifetime Fellow
- Cognitive Science Society : Member, Governing Board
Links
Languages
- Spanish
- English
Articles
Exploring the effects of real-time feedback on collaborative processes to enhance collective intelligence in teams
Collective Intelligence2026
A growing body of research has identified behavioral markers of collective intelligence to diagnose the quality of group collaboration in real time. Prior work suggests that real-time collaborative process metrics may be used not only to measure and predict the collective intelligence of a group, but also to improve team performance through targeted interventions.
Toward a science of human–AI teaming for decision making: A complementarity framework
PNAS Nexus2026
As artificial intelligence (AI) becomes embedded in critical decisions involving health, safety, finance, and governance, the key challenge is no longer whether humans and AI will collaborate, but rather how to structure this collaboration to achieve true complementarity. Human–AI complementarity refers to the conditions under which human–AI teams outperform either humans alone or AI systems alone.
Toward Complementary Intelligence: Integrating Cognitive and Machine AI
Current Directions in Psychological Science2026
This article calls for complementary human-AI intelligence. Rather than redefining intelligence to fit machine capabilities, we argue for designing AI that complements and extends human cognition. We distinguish between cognitive AI, which is grounded in cognitive science to model human perception, learning, and decision-making, and machine AI, which achieves large-scale performance through data-driven optimization.
Improving online anti-phishing training using cognitive large language models
Computers in Human Behavior2025
Training the public to recognize phishing emails is challenging because attackers increasingly use Large Language Models (LLMs) to craft more convincing scams. Although previous research suggests that fully LLM-generated phishing emails are easier for humans to identify, recent models like GPT-4 used in tandem with prompt engineering methods could generate phishing emails that are more difficult to detect.
A cognitive approach to human–AI complementarity in dynamic decision-making
Nature Reviews Psychology2025
As artificial intelligence (AI) becomes increasingly integrated into complex decision-making environments, there is a growing need to develop AI systems that complement human capabilities. AI and humans offer distinct strengths: AI excels at processing large datasets, identifying statistical patterns and optimizing predefined objectives, whereas humans are skilled at navigating uncertainty, novelty and interpersonal challenges.
Research Focus
Dynamic Decision Making
(DDM)
Dynamic Decision Making (DDM) refers to how people assess and choose among alternatives in environments that evolve over time. In these settings, decisions unfold sequentially, under uncertainty, and with feedback that shapes future choices.
Our research shows that people rely heavily on experience when navigating dynamic tasks. Rather than computing optimal solutions from scratch, they draw on memories of past situations, retrieve actions that worked before, and adapt those strategies based on outcomes. This process is formalized in Instance-Based Learning (IBL) theory, a cognitive framework that enables us to build computational models of human decision-making.
Dynamic Decision Making Laboratory
The study of decision making in dynamic environments. Dynamic decision making is characterized by the need to make multiple, interdependent, real-time decisions while adapting to external changes and using our past experience.
