Understanding and Enhancing Human Decision Making in a Dynamic World
In today’s world, decisions are made in environments that are complex, fast-changing, and uncertain. Options do not appear all at once—they unfold over time, often under pressure, requiring people to explore, adapt, and act with limited information. Whether navigating information overload, operating under time constraints, or responding to unpredictable change, effective decision-making is both challenging and critical.
At the Dynamic Decision Making Laboratory (DDMLab), we study how people make decisions in these dynamic environments. Our research develops and tests cognitive theories that explain how individuals learn from experience, adapt over time, and make sequential choices under uncertainty. We formalize these processes through computational models that capture the mechanisms underlying human decision-making.
By linking theory to application, we use these models to inform the design of tools and systems that improve decision-making in high-stakes domains, including healthcare, emergency response, cybersecurity, and beyond.
Our Research Approach: Bridging Human Behavior and Cognitive Modeling
At DDMLab, we integrate controlled behavioral experiments with cognitive computational modeling to understand—and ultimately improve—human decision-making in dynamic environments.
In our experiments, individuals and teams engage in tasks that evolve over time and space, requiring continuous adaptation under uncertainty. These studies reveal the strategies people use, how they learn from experience, and where systematic biases or limitations emerge.
We complement this work with cognitive models that simulate decision-making processes through formal algorithms. These models operate in the same tasks as humans, enabling direct comparisons across key dimensions such as learning, risk sensitivity, adaptation, and performance relative to optimal benchmarks.
By aligning human behavior with model predictions, we identify the cognitive mechanisms that drive decisions in dynamic settings. These insights guide the development of practical solutions in domains including cybersecurity, climate resilience, phishing prevention, and human–AI interaction.
Instance-Based Learning: How Experience Shapes Decisions
At DDMLab, we use Instance-Based Learning Theory (IBLT) () to explain how people make decisions from experience. In dynamic situations individuals rely on memories of past situations—instances—to guide their choices.
When facing a new decision, people retrieve similar past experiences, evaluate the outcomes associated with those experiences, and select the option that appears most promising. After acting, the outcome is stored as a new instance, continuously updating the knowledge base that supports future decisions. This cycle of retrieval, choice, and feedback enables learning and adaptation in dynamic environments.
IBLT formalizes this process through a mathematical framework grounded in the The model specifies how memory is shaped by recency, frequency, and similarity—factors that determine which experiences are retrieved and how strongly they influence decisions.
To make these ideas operational, we developed PyIBL, a Python-based platform for building and testing IBL models. PyIBL allows researchers and practitioners to simulate human decision-making, evaluate competing hypotheses, and apply IBLT to real-world problems. Visit our Cognitive Modeling page to access PyIBL, documentation, and example models.
We evaluate IBLT—and alternative theories of decisions from experience—through systematic model comparison. Models are assessed based on how well they reproduce observed human behavior across tasks, providing a rigorous foundation for theory development and application.
Latest Projects: Behavioral Cybersecurity
Impact of Cognition on Cyber Behavior
This research program aims to improve cybersecurity by understanding how human cognition impacts cyber behavior and could affect cyber actors' success in network attack activities. Specifically, well-established cognitive patterns, such as loss aversion and the representativeness bias, are be investigated as potentially mitigating factors in the efficacy of cyber attack behavior. This research contributes to the broader goals of improving cyber defense practices by delaying and thwarting attacks.
In this work we:
- Replicate cognitive biases such as availability, endowment, and recencyin cyber domains.
- Identify behavioral signatures linked to cognitive biases in capture-the-flag cyber attack games.
- Insert cyber-isomorphs into an attack kill chain environment.
- Utilize the CyberVAN testbed with penetration testers to investigate triggering cognitive biases as mitigating factors in the efficacy of cyber-attack behavior.
Phishing Training and Detection
Phishing emails continue to evade automated detection and are a major way in which attackers get into various organizations' networks. Phishing is a form of deception that relies largely on social engineering tactics, where attackers take advantage of human weaknesses such as: reacting to familiar senders, to immediate requests, and to emotional requests. Based on IBLT, we know that these phishing classification decisions are influenced by the type of experiences people have. For example, end-users make decisions based on the similarity of features of a current email to features of emails they have received in the past. Importantly, phishing emails often mimic benign emails—meaning that decision makers, who are influenced by typical memory effects such as recency and frequency of past instances, are susceptible to the cognitive biases that emerge from these very processes.
An was built to emulate end-used classification decisions of emails (i.e., as phishing or benign) and the results from this model were compared to the classification decisions from humans in an email processing task. We are working on building training scenarios that take advantage of the insights of our model.
Defense Strategies in a Repeated Binary Choice Task
Adapting to dynamic environments poses significant challenges for humans, even in seemingly simple scenarios, such as repeated binary choice tasks. Researchers have explored different directions to address this issue, including the use of cognitive models to predict human adaptive capabilities. This research investigates the effectiveness of interventions and the role that an Instance-Based Learning (IBL) cognitive model could play in facilitating adaptation to changing conditions. The goal of this work is to design defense strategies to influence human choices in real time.
During these tasks, attackers repeatedly attempt to find the highest reward in one of two boxes. We construct an Instance Based Learning (IBL) cognitive model that tracks human behavior and makes one-step-ahead predictions of human decisions. Finally, we introduce an intervention based on predicted choices of the participant and measure post-intervention changes.
Human-Machine Collaborations in Autonomous Cyber Operations
IBLT is in a theory of individual decision making, and groups learn by the learning of individual group members. We have demonstrated that group effects and dynamics can be captured by the aggregation of individual members of a group and their interdependencies. We first expanded IBL models to capture the interdependencies in social dilemmas the resulting effects on the .
In this work we use the same essential elements of IBLT with an added dynamic function representing the social value (i.e., the regard that each individual has for the other's outcomes). We use the Prisoner's Dilemma and other 2-person games (e.g., Rock-Paper-Scissors). We are also expanding this idea to large networks of various structures.
We are also currently advancing the concept of interactions between pairs of individuals to elucidate interactions between humans and machines in groups. For example, we have constructed an architecture in which IBL models develop by observing other agents.
Latest Projects: Human-Machine Collaborations
Disaster Relief Management Decision Making
This research program aims to test how real professional disaster relief managers make decisions about resource allocation and information gathering during natural disasters.
In this work we examine questions of:
- Perceptual Aggregation: How do participants internally weigh different damage indicators when rating a disaster-effected asset?
- Allocation Trade-Offs: After forming damage perceptions, how do people prioritize which locations receive scarce follow-up resources?
- Temporal Drift: Do those weights or priorities shift after the full tabletop simulation, once participants have experienced realistic coordination stress?
Answering these questions supports the development of learned social welfare functions that could inform intelligent decision support tools for future disaster response.
Integrating Theory of Mind Capabilities in AI Partners
As autonomous agents become more ubiquitous, it is important that we understand how best to design AI agents for effective human-AI collaboration. In line with that goal, much work has been devoted to developing AI agents that are adaptive to a variety of situations and to human partners. Theory of Mind (ToM) has been suggested as a solution to achieve implicit coordination - the process of aligning actions without communication - between team members.
These reasearch projects facilitate Human/AI coordination by building Theory of Mind capabilities in AI partners by:
- Integrating algorithms for preference inference into the k-level framework.
- Examining coordination by an AI partner relying on predictions of the human agent's actions, which in turn relies on both an appropriate and fine-tuned (Instance-Based Learning) model of human agents.
- Looking for individual differences in ToM in areas of cognitive processing, emotional intelligence, and spatial awareness.
Cognitive Models of Behavior in Sequential Decision Tasks
To understand how people make sequential decisions in various tasks involving balancing exploration and exploitation, we develop cognitive models of their behavior in these tasks.
In this research prgram we:
- Introduce a novel sequential stopping task to shed light on how, when, and why people decide to stop exploring.
- Systematically examine some of the factors that may influence stopping behavior and validate the predictions of our cognitive model.
- Investigate interventions leveraging wisdom of crowdsaggregation techniques to provide personalized, cognitive AI-driven recommendations for when to stop searching.
Cognitively Aware Reinforcement Learning
In collaborative domains, a desirable attribute of AI partners is the ability to be adaptive to the behaviors and preferences of humans. The goal of this research is to investigate how cognitive models can be used in tandem with reinforcement learning (RL) agents to learn policies that complement human behavior.
To achieve this we:
- Incorporate cognitive models into the RL training and testing pipelines to see how such models can improve performance in cooperative tasks.
- Test these models with human proxies and real humans, and analyze their behavior using collaborative fluency metrics, to see how well they learn collaborative policies.
