Library · Simulation & modelling
What Agent-Based & Multi-Agent Simulation got wrong, from 59 theses
Summary written by a model from the records below: Across these studies, researchers encountered substantial obstacles when designing, scaling, and calibrating agent-based and multi-agent systems. Many simulations broke down due to inaccurate behavioral assumptions, excessive computational bottlenecks, or coordination algorithms that underperformed simple heuristics. These records come from graduate theses, 2021 to 2026, each labeled with its degree where the repository states it, and each links to its thesis. They were extracted by language models reading the full text, so treat each as a lead to read, not a verdict.
Plus 10 problems the authors left open, listed at the end.
Failure modes, by theses reporting them
Select a mode to jump to its records.
Behavioral and decision-making rules fail to capture realistic agent strategies and human dynamics
Agent models relying on simplified rules, unregularized reinforcement learning, or static choice sets failed to reflect strategic adaptation, resulting in erratic behaviors and inaccurate predictions. These formulations also degraded coordination with human counterparts and produced responses that fell well outside empirical benchmarks.
Tried and failed
exponential discounting heterogeneous-agent modeling applied to empirical consumption-wealth sensitivity to policy. Outcome: did not generalise. Reason: produced response coefficients significantly outside empirical confidence intervals
Essays in Macroeconomics and Stabilization Policy · Harvard · PhD
Tried and failed
single agent opponent modeling applied to asymmetric multiagent games with deceptive adversaries. Outcome: overfit. Reason: overfits to simple training behaviors, failing to detect deceptive opponents during evaluation
Robust and Scalable Multiagent Reinforcement Learning in Adversarial Scenarios · MIT · PhD
Tried and failed
two-asset heterogeneous-agent incomplete-markets modeling applied to household consumption-savings distribution. Outcome: did not generalise. Reason: incorrectly predicts extreme, persistent marginal propensities to consume across constrained and unconstrained agent groups
Essays in Macroeconomics with Heterogeneous Households: Consumption, Income Risk, and Government Debt · Harvard · PhD
Tried and failed
robust optimization in multi-agent game theory applied to autonomous vehicle decision making. Reason: Failed to find optimal solutions due to lacking probabilistic modeling of opponent reasoning uncertainty.
Socially Compatible Behavior Prediction, Decision-making, and Control for Autonomous Vehicles in Mixed Traffic · Texas Tech · PhD
Tried and failed
Linear conflict count extrapolation models applied to multi-agent trajectory management system performance. Outcome: no signal. Reason: Oversimplified geometrical interaction models failed to capture complex emergent dynamics of decoupled 4D trajectory planning
Conceptual-Level Analysis and Design of Unmanned Air Traffic Management Systems · Georgia Tech · PhD
Tried and failed
partial portfolio investment constraints on subset agents applied to multi-agent oligopolistic asset markets. Outcome: worse than baseline. Reason: shifts market power to unconstrained strategic agents, worsening overall market liquidity
Banking and optimal design · UT Austin · PhD
Tried and failed
unregularized reinforcement learning applied to human-agent multi-agent coordination. Outcome: did not generalise. Reason: agents diverged from human play styles, reducing action prediction accuracy and cooperation
Building Strategic AI Agents for Human-centric Multi-agent Systems · MIT · PhD
Tried and failed
transferring prior bias weights from prediction to planning applied to model-based reinforcement learning agents. Outcome: did not generalise. Reason: inductive biases optimized for state prediction failed to support effective sequential decision making and planning
Theory-based reinforcement learning: A computational framework for modeling human inductive biases in complex decision making domains · Harvard · PhD
Tried and failed
adversarial inverse reinforcement learning with heterogeneous data applied to agent trajectory modeling across behavioral regimes. Outcome: worse than baseline. Reason: mixing data from distinct behavioral regimes degraded policy learning and prediction accuracy
A socio-technical approach to crowd management: understanding decision-making and movement patterns with virtual reality and machine learning · Imperial · PhD
Tried and failed
Regret aversion modeling applied to spatial worker relocation decisions. Outcome: did not generalise. Reason: Wrongly predicted over-movement from distant rather than nearby agents under spatial information sharing
Information Sharing and Operational Transparency on On-Demand Service Platforms · Georgia Tech · PhD
Tried and failed
sentiment-driven persona conditioning in multi-agent systems applied to multi-agent deliberative debate. Outcome: worse than baseline. Reason: lacked persuasive reasoning and negotiation ability
Toward Deliberative AI: Multi-Agent LLMs for Real-World Reasoning · Virginia Tech · Master's
Tried and failed
counterfactual simulation with fixed choice sets applied to mechanism design policy evaluation. Outcome: did not generalise. Reason: agents strategically adjust choice lists in response to policy changes rather than holding preferences fixed
Essays in Economics of Education · MIT · PhD
Tried and failed
agent-based simulation and structured peer deliberation applied to teaching iterative consensus-building and epistemology. Outcome: did not generalise. Reason: learners defaulted to seeking single universal procedural rules rather than grasping iterative scientific consensus-making
Why Trust Science? Reimagining Science Education in the Era of Science Skepticism and Misinformation · Penn · PhD
Tried and failed
agent-based segregation modeling with high tolerance thresholds applied to spatial segregation and integration dynamics. Outcome: did not generalise. Reason: intolerant preference thresholds non-monotonically produced integrated or less segregated steady states instead of universal segregation
Simulating social dynamics: a computer aided analysis of emergent behaviour in multi-agent systems · Imperial · PhD
Tried and failed
anthropomorphic interface design for conversational agents applied to human-agent game-theoretic cooperation. Outcome: worse than baseline. Reason: anthropomorphism provoked steeper retaliatory defection following perceived betrayal than robotic baselines
Challenging the Benefit of Anthropomorphism on Human-AI Collaboration with AI Voice Agents · Georgia Tech · PhD
Lost to a baseline
In Example 6.5 (Strange betting), Path-Specific Objectives (PSO) achieved sub-optimal performance (failed to learn anything) because utility required influencing the other agent, whereas shielding learned the optimal-honest policy.
AI deception: formalising and evaluating deception in AI agents · Imperial · PhD
Lost to a baseline
Recommender agent trained by deep reinforcement learning demonstrated lower human performance improvement compared to baseline agents exhibiting human-like behavior.
Reciprocal human-machine learning in manufacturing · TU Wien · PhD
Considered and rejected
Considered and rejected: Rejected modeling agent search propensity as dynamic individual choice in the ABM due to lack of theoretical clarity on switching triggers, fixing agent types as explorer or imitator.
Three Essays on Exploration and Exploitation: Behavioral Insights and Individual Decision-Making · University of Trento · PhD
Decentralized and complex multi-agent coordination underperforms simple greedy baselines or introduces system friction
Elaborate multi-agent coordination strategies often introduced coordination delays, turbulence, or deadlocks that degraded performance below simple greedy heuristics and centralized baselines. When routing or merging under congestion, over-penalizing traffic or assuming complex opponent reasoning created severe detours and lower overall social welfare.
Tried and failed
higher-order cognitive hierarchy modeling applied to multi-agent bounded rationality games. Outcome: worse than baseline. Reason: Overestimating opponent reasoning depth yields suboptimal responses against lower-level strategic agents.
Control and game-theoretic methods for secure cyber-physical-human systems · Georgia Tech · PhD
Tried and failed
multi-agent reinforcement learning group coordination applied to continuous multi-parameter process control. Outcome: worse than baseline. Reason: decentralized multi-agent coordination achieved sub-optimal transient control performance compared to a centralized single-agent baseline
Reinforcement learning applied to water resource recovery facilities for process optimization and control consolidation · Iowa State · PhD
Tried and failed
Multi-Agent Deep Deterministic Policy Gradient applied to multi-robot decentralized sequential decision-making. Outcome: worse than baseline. Reason: struggled with partial observability and coordination under uncertainty despite extensive hyperparameter tuning
Sequential Decision-Making for Multi-Robot Systems with Real-World Uncertainty using Rollout-based Reinforcement Learning · Harvard · PhD
Tried and failed
Traffic-following path planning applied to multi-agent decentralized routing. Outcome: worse than baseline. Reason: High traffic-following weight caused excessive and unnecessary detours in low-density regimes
Nature-inspired self-organization in autonomous multi-agent systems · UT Austin · PhD
Tried and failed
high traffic-penalty weighting in routing algorithms applied to dense multi-agent path planning. Outcome: worse than baseline. Reason: capacity saturation occurred because routing lacked trajectory prediction, causing congestion across alternative paths
Nature-inspired self-organization in autonomous multi-agent systems · UT Austin · PhD
Tried and failed
selective penalties on subset of routing agents applied to non-series-parallel congestion networks. Outcome: worse than baseline. Reason: traffic redistribution across alternative network paths increased equilibrium latency for unpenalized agents
Essays on Economics and Computer Science · Cornell · PhD
Tried and failed
Derivative-free continuous relaxation for task allocation applied to multi-agent routing and scheduling. Outcome: worse than baseline. Reason: Greedy path planning dominates makespan when task-to-agent ratio is large, limiting optimization gains
Distributed Heterogeneous Multi-robot Task Allocation in Communication-limited Environments · Georgia Tech · PhD
Tried and failed
explicit tactical coordination in multi-agent reinforcement learning applied to high-density swarm adversarial engagements. Outcome: worse than baseline. Reason: explicit multi-agent coordination was overwhelmed and outscored by decentralized greedy baseline policies in dense combat
Coordinating Team Tactics for Swarm-vs.-Swarm Adversarial Games · Georgia Tech · PhD
Tried and failed
centralized inventory pooling mechanism applied to multi-agent supply chain coordination. Outcome: worse than baseline. Reason: Behavioral ordering deviations and competitive dynamics eroded theoretical pooling efficiencies in human-subject settings.
Experimental Studies on Supply Chain Contracting · Cornell · PhD
Tried and failed
optimal control strategy under latency applied to multi-agent pursuit-evasion games. Outcome: worse than baseline. Reason: computational delay in calculating optimal intercept points degraded performance below simple heuristics
Perception-Action Loop for Multi-Robot Systems with Deep Learning · Penn · PhD
Tried and failed
decentralized multi-agent planning under low communication stochasticity applied to multi-robot inspection coordination. Outcome: worse than baseline. Reason: Low communication dropouts caused multi-agent deadlocks, whereas higher stochasticity helped break symmetrical decision deadlocks.
Sequential decision-making and active sensing for autonomous outer space operations · UT Austin · PhD
Tried and failed
randomized resource selection instead of sequential ordering applied to decentralized multi-agent resource allocation. Outcome: worse than baseline. Reason: random selection reduced overall social welfare compared to structured sequential choice
Scalable Multi-agent Coordination and Resource Sharing · EPFL · PhD
Excessive computational complexity and scaling bottlenecks prevent large-scale multi-agent execution
Simulating large populations or training centralized multi-agent policies created severe computational bottlenecks and communication overheads that scaled exponentially with agent counts. Because running individual-level agents across expansive networks proved intractable, practitioners frequently abandoned full agent-based simulations in favor of aggregated or rasterized representations.
Tried and failed
standard belief propagation on joint distribution applied to multi-commodity network routing problems. Outcome: infeasible cost. Reason: computational complexity scales exponentially with the number of agents or paths
Advances in Algorithms for Sampling, Optimization and Inference in Disordered Systems · EPFL · PhD
Tried and failed
iterative greedy backward heuristic for bin packing applied to dynamic multi-hub parcel consolidation. Outcome: too slow. Reason: computational scaling limitations when executed within an agent-based simulation of large real-size networks
Hyperconnected Parcel Logistics: Planning and Assessment · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Rejected standard Agent-Based Modeling (ABM) and detailed game-theoretic models for large-scale mathematical network optimization due to prohibitive computational complexity and scaling limitations.
Integrating Optimisation Models and Behavioural Insights for Effective Transport Evacuation Strategies · University College Dublin · PhD
Considered and rejected
Considered and rejected: Centralized training for large-scale MARL traffic networks rejected due to excessive communication overhead, coordination complexity, and computational bottlenecks across thousands of agents.
Adaptive intelligent traffic control systems in smart city · Cranfield · PhD
Considered and rejected
Considered and rejected: Rejected Agent-Based Simulation as the primary baseline due to excessive computational resource demands for broad design-space exploration sweeps.
A METHODOLOGY FOR THE MODULARIZATION OF OPERATIONAL SCENARIOS FOR MODELLING AND SIMULATION · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Rejected a fully scaled agent-based model of monarch butterfly migration due to excessive computational demands in NetLogo, choosing a generalized unitless pinch-point migration model instead.
Behavioral and Geophysical Factors Influencing Success in Long Distance Navigation · Duke · Doctorate
Considered and rejected
Considered and rejected: Rejected agent-based mobility simulations in favor of grid-based raster modeling due to computational and large-scale data constraints across Europe.
Considered and rejected
Considered and rejected: Rejected treating drivers and customers at an individual agent-based level during vehicle rebalancing optimization due to computational tractability, preserving regional spatial aggregation instead
Towards A Robust Integrated Urban Mobility System: Public Transit and Ride-Sharing Systems · MIT · PhD
Considered and rejected
Considered and rejected: Agent-Based Simulation (ABS) and System Dynamics (SD) rejected; ABS added excessive computational demand and SD lacked discrete event scheduling variability
DEVELOPING A SIMULATION-BASED DECISION SUPPORT TOOL TO IMPROVE CANCER CARE RESOURCE USE AND PATIENT ACCESS · Saskatchewan · PhD
Tried and failed
nonparametric simulation-based iterative dynamic programming applied to heterogeneous-agent equilibrium models. Outcome: too slow. Reason: linear aggregate dynamics without complex market clearing make parametric approximations computationally faster
Essays On Heterogeneity In Macroeconomics · Penn · PhD
Considered and rejected
Considered and rejected: Rejected centralized global multi-agent MDP optimization due to exponential computational complexity; adopted decentralized inter-agent Gaussian reward scaling
Hybrid Sensor Networks for Active Monitoring: Collaboration, Optimization, And Resilience · Georgia Tech · PhD
Improper representation of physical environments and inter-agent couplings leads to simulation breakdown
Treating physical environments merely as peer agents or ignoring spatial-temporal coupling caused models to underestimate collective dynamics and experience non-convergence. In addition, assuming stationary opponents or failing to account for asynchronous actions led to erratic agent interactions and ill-posed optimization landscapes.
Tried and failed
independent uncoupled agent contraction modeling applied to bulk volume reduction of active networks. Reason: neglecting collective structural transmission and coupling drastically underestimated macroscopic network contraction magnitude
Mesoscale Modeling and Probing of Blood Clot Contraction · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Treating the environment as merely another peer agent (as in classical Interpreted Systems), rejected because it fails to represent physical properties and dynamics appropriately
A Framework for Simulating and Analysing Multi-Agent Systems in YODA · University of Camerino · PhD
Considered and rejected
Considered and rejected: Using belief Biφ := Ki(correcti -> φ) or BiE♢B start as action triggers, rejected because faulty agents cannot be constrained and agents lack unconditional beliefs about others' beliefs
Agents’ knowledge and its limits in byzantine fault-tolerant distributed systems · TU Wien · PhD
Considered and rejected
Considered and rejected: Rejected deterministic variational least-squares trajectory recovery due to ill-posedness and high-dimensional multi-modal optimization landscapes caused by agent symmetry
MULTI-AGENT SYSTEMS: OBSERVABILITY, CLASSIFICATION AND CLUSTERING PREDICTION · Johns Hopkins · PhD
Considered and rejected
Considered and rejected: Rejected relying on single-agent RL applied to multiple agents because non-stationary environmental dynamics prevent convergence to optimal policies.
Leveraging Information Sharing for Satellite Navigation and Coordination · MIT · PhD
Tried and failed
relaxing baseline travel mode accessibility constraints applied to iterative multimodal dynamic network equilibrium simulation. Outcome: did not converge. Reason: agents without viable transport alternatives caused unrealistic behaviour and non-convergence in iterative assignment
Simulation and optimisation of dynamic multimodal traffic network with shared mobility systems · Imperial · PhD
Tried and failed
Python and R agent-based modeling frameworks applied to GIS and network-based spatial simulation. Reason: Frameworks lacked mature features for complex network and GIS-based simulation requirements.
Assessing Health Vulnerability to Air Pollution in Seoul Using an Agent-Based Simulation · Cambridge · PhD
Considered and rejected
Considered and rejected: Rejected simulating hierarchical networks in agent-based models because formal top-down constraints lack empirical relevance for open social media follower networks
How social media affects political action: the effects of digital network structures and motivations on movement participation · University of Washington · PhD
Considered and rejected
Considered and rejected: Rejected naive single-step multi-agent MDP state transitions in favor of Semi-MDP discrete-event formulations to handle asynchronous continuous-time action durations.
Enhancing Teamwork in Multi-Robot Systems: Embodied Intelligence via Model- and Data-Driven Approaches · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Rejected treating multi-agent learning with equal priority or assuming the opponent is a stationary environment (simultaneous gradient descent-ascent in MADDPG) in favor of hierarchical Stackelberg modeling.
Certifiable Algorithms for Reinforcement Learning: Safety-Critical and Game-Theoretic Perspectives · University of Washington · PhD
Borrowed literature parameters and aggregate macro calibrations fail to match micro-level agent distributions
Calibrating models using borrowed literature parameters or purely aggregate macro targets produced velocity and welfare distributions that contradicted empirical observations. Optimizing solely for aggregate macro outputs washed out the impact of micro-level decision logic and failed to predict critical transition points.
Tried and failed
calibrating structural model with literature-borrowed parameters applied to multi-agent network welfare analysis. Outcome: did not generalise. Reason: borrowed parameters produced contradictory welfare directions across heterogeneous groups
Essays in macroeconomics · UT Austin · PhD
Tried and failed
using literature-reported interaction parameters directly applied to agent-based trajectory simulation calibration. Outcome: did not converge. Reason: literature values failed to generalize to the specific open-field environmental dynamics
Video-Based Parameter Calibration and Virtual Simulation of Pedestrian Crowd Dynamics · Virginia Tech · Master's
Tried and failed
macro-level statistical calibration of agent-based models applied to agent decision-making dynamics. Outcome: no signal. Reason: optimising solely for aggregate macro outputs washed out the impact of micro-level decision logic
Agent-based modelling of people movement using mixed methods approach · Imperial · PhD
Tried and failed
agent-based modeling with empirically derived parameters applied to healthcare access disparities by geographic distance. Outcome: did not generalise. Reason: remoteness did not act as a uniform barrier, with long-distance agents exhibiting higher utilization rates
U.S. Rural-Urban Breast Cancer Screening Inequities: Leveraging Contextual Heterogeneity to Identify Solutions · Harvard · PhD
Tried and failed
agent-based social cognitive modeling applied to predicting emergent group structure metrics. Outcome: no signal. Reason: mean follower self-schemas lacked predictive power for network density or centralization across interactions
Using Agent-Based Modeling to Test and Integrate Process-Oriented Perspectives of Leadership Emergence · Virginia Tech · Doctorate
Tried and failed
integrate-and-fire dynamics with stochastic coupling applied to agent-based financial market simulations. Outcome: no signal. Reason: interaction strength thresholds failed to consistently trigger bubble collapse or predict price deviations
Integrate-and-fire models of financial markets · Imperial · PhD
Tried and failed
approximate aggregation using parametric forecasting rules applied to heterogeneous agent macroeconomic models. Outcome: did not generalise. Reason: lack of general equilibrium price smoothing caused forecasting rules to fit poorly
Essays On Heterogeneity In Macroeconomics · Penn · PhD
Agent-based simulation is abandoned for aggregate or continuous paradigms due to data deficits or structural scope
Authors rejected agent-based models when early deployments lacked granular individual behavioral data or when operational random behaviors could not be modeled reliably. In these settings, researchers preferred system dynamics, discrete event simulation, or network science to better represent aggregate structural accumulations and queuing dynamics.
Considered and rejected
Considered and rejected: Rejected agent-based modeling for policy simulation because the early-stage deployment of blockchain-DPP systems lacked granular empirical behavioral data at the individual stakeholder level.
Optimization of Reverse Supply Chain For End-of-life Products · Cranfield · PhD
Considered and rejected
Considered and rejected: Rejected agent-based modeling (ABM) in favor of system dynamics causal mapping and stock-flow modeling because food supply chain flows and institutional structural accumulations were better captured at aggregate levels.
Food Justice Transitions in Buffalo, NY: The Role of Values-Based Approaches in Social Movements · University at Buffalo · PhD
Considered and rejected
Considered and rejected: Rejected using System Dynamics (SD) or Agent-Based (AB) simulation, choosing Discrete Event Simulation (DES) to accurately capture discrete timestamped tasks and stochastic queue dynamics.
Modeling of Urban Freight Deliveries; Operational Performance at the Final 50 Feet · University of Washington · PhD
Left open by the authors
Problems the authors named and did not get to.
Left open
Develop an agent-based behavioral simulation model to simulate pedestrian dynamics under varying thermal comfort conditions in urban spaces. Blocker: Lacks specific behavioral parameters, empirical calibration datasets, and formal agent decision-rule specifications.
Urban comfort and connectivity in hot-climate cities: evaluating public space usability with the UCCI framework in Riyadh · Nottingham · PhD
Left open
Model bacteria-substrate surface interactions using agent-based discrete element simulations to study CaCO3 grain morphologies. Blocker: Lacks specific interaction parameters and formulation details for the proposed agent-based discrete element model.
Multiscale Structure and Dynamics of Engineered Biological and Metallic Materials: Integrating Molecular Dynamics and Machine-Learned Models · Cornell · PhD
Left open
Incorporate agent-based simulation and stochastic optimization analysis tools into the circular remanufacturing decision framework. Blocker: Lack of specific modeling requirements, parameter distributions, agent behaviors, or mathematical formulations
Decision-Making Framework for Circular Economy in Remanufacturing · University of Windsor · PhD
Left open
Develop automated or inverse generative AI methods to parameterise agent logic in agent-based models of people movement. Blocker: Lacks specific target architectures, parameter formulations, and validation benchmarks
Agent-based modelling of people movement using mixed methods approach · Imperial · PhD
Left open
Implement the Agent sub-model (Model A) to manage natural language representations, perception, and composite agent decision performance in SAbMDE. Blocker: Lacks specific formal design specifications and clear evaluation targets for Model A
Development Cycle Modeling and Risk Calculation · Texas Tech · PhD
Left open
Couple integrated transit time distribution hydrologic models with agent-based models of human behavior to simulate watershed salt application scenarios. Blocker: Lacks specific target watershed, behavioral rules, agent-based formulation, or performance targets
Unveiling Causal Links, Temporal Patterns, and System-Level Dynamics of Freshwater Salinization Using Transit Time Distribution Theory · Virginia Tech · Doctorate
Left open
Benchmark the performance of the COVID-19 transfer learning framework against agent-based epidemiological models. Blocker: None
Social Network Analysis: A Machine Learning Approach · University of Windsor · PhD
Left open
Validate individual operational assumptions used to formalize and reconcile policy process theories within the agent-based simulation model. Blocker: No specific empirical datasets, experimental designs, or validation criteria are provided for the theoretical assumptions
Exploring policy change through agent-based simulation · EPFL · PhD
Left open
Formulate stochastic extensions of compartmental biocontrol models and evaluate them on alternative plant-pathogen-biocontrol agent systems. Blocker: Lacks specific target systems, experimental datasets, or concrete mathematical formulation for stochasticity.
Using Modelling to Optimise the Use of Biological Control Agents Against Soil-Borne Plant Pathogens · Cambridge · PhD
Left open
Develop a formal model for how rational agents commensurate credence and depth of ingression when evaluating paradox premises. Blocker: Lacks specific mathematical framework, target metric, or concrete formal constraints for the commensuration mechanism
God and other enigmas · UT Austin · PhD
Who has worked on this
Authors of the public theses cited on this page, most-cited first.
- He-in Cheong · Imperial · PhD · 2023
Agent-based modelling of people movement using mixed methods approach - Hanbaek Lee · Penn · PhD · 2021
Essays On Heterogeneity In Macroeconomics - Roberto Colarieti · Harvard · PhD · 2026
Essays in Macroeconomics with Heterogeneous Households: Consumption, Income Risk, and Government Debt - Noémie Alice Yvonne Ségolène Jeannin · EPFL · PhD · 2026
Geospatial modelling of the potential for coupling electric vehicle charging with photovoltaic electricity generation in Europe - Faria Rahman · Virginia Tech · Master's · 2026
Video-Based Parameter Calibration and Virtual Simulation of Pedestrian Crowd Dynamics - Mona Alnimer · Nottingham · PhD · 2026
Urban comfort and connectivity in hot-climate cities: evaluating public space usability with the UCCI framework in Riyadh - Sidney Tammie Scott-Sharoni · Georgia Tech · PhD · 2025
Challenging the Benefit of Anthropomorphism on Human-AI Collaboration with AI Voice Agents - Liming Zhao · Cornell · PhD · 2025
Multiscale Structure and Dynamics of Engineered Biological and Metallic Materials: Integrating Molecular Dynamics and Machine-Learned Models - Xiangmin Yang · Imperial · PhD · 2025
A socio-technical approach to crowd management: understanding decision-making and movement patterns with virtual reality and machine learning - Jennifer L. Cruz · Harvard · PhD · 2025
U.S. Rural-Urban Breast Cancer Screening Inequities: Leveraging Contextual Heterogeneity to Identify Solutions - David Christian Aurel Meine · Imperial · PhD · 2025
Integrate-and-fire models of financial markets - Sydney Dolan · MIT · PhD · 2025
Leveraging Information Sharing for Satellite Navigation and Coordination
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