Research

I am broadly interested in the analysis and control of multi-agent systems, where self-interested individuals make decisions in an environment of other decision-makers. My research leverages techniques from game theory, control theory, optimization, and network science. Below are a few topical areas of my research.

Strategic decision-making in adversarial environments

Today's critical infrastructures and complex systems rely on the operation and communication between multiple, interconnected sub-components. This dependence becomes a liability as it presents many new ways for adversaries to compromise the system. Effective security implementations are thus an increasingly important aspect in system design. Ensuring security comes with a host of challenges. For one, the amount of available resources to devote to security is limited, and hence strategic allocation becomes an important consideration. Moreover, the strategic behavior of an adversary may be unknown due to informational limitations. Thus, security measures must be effective even under uncertainty.

Our work focuses on the analysis of optimal or near-optimal resource allocation control policies and algorithms. They provide useful insights that inform resource-aware security design, cost-effective decision-making, and scalable security implementations. We have developed "Colonel Blotto games" as a unifying framework to study various elements in competitive resource allocation problems. These tools have found applications in cybersecurity, market strategy, social influence, and public safety. An important element in these applications are informational asymmetries that arise between competing parties. A central theme of our research is characterizing the "value of information": the performance improvement that is attainable from having additional information.

This work is supported by NSF grant ECCS-2346791. Recent Publications:

K. Paarporn, S. Xu. "Preventive-Reactive Defense Tradeoffs in Resource Allocation Contests" IEEE Control Systems Letters, v.8, 2024
F. Shojaei, S. Xu, K. Paarporn. "Optimizing Preventive and Reactive Defense Resource Allocation with Uncertain Sensor Signals". 61st Annual Allerton Conference, 2025.
G. Diaz-Garcia, K. Paarporn, J. R. Marden. "The value of compromising strategic intent in general lotto games". American Control Conference, 2025.

Dynamics of multi-agent learning

Societal outcomes are driven by the decision-making of large populations of agents. Individual agents act as independent decision-makers whose actions are influenced by incentives, interactions with others, access to information, and other cognitive factors. A central underlying concept is the "tragedy of the commons", which are scenarios where individual self-optimizing behaviors lead to undesirable collective behaviors, e.g. the collapse of common resources, the outbreak of a pandemic, or the failure of task coordination.

A primary goal is to understand when and how tragedies occur in large populations of decision-makers. In particular, we seek to identify conditions on individual-level factors, e.g. certain types of agent learning rules, incentive control policies, or network structure, for which low-quality collective behaviors may be avoided. We also seek to characterize influence and control mechanisms that can improve societal-level benefits.

We investigate these directions by examining feedback mechanisms between individual incentives for action and their impacts on an environmental state. That is, individual actions can affect environmental conditions, and in turn the changing environment shapes incentives for future actions. We find such co-evolutionary processes provide rich and complex dynamics.

K. Paarporn, C. Eksin. SIS epidemics coupled with evolutionary social distancing dynamics. American Control Conference, pp. 4308-4313, 2023.
K. Paarporn. Non-myopic agents can stabilize cooperation in feedback-evolving games. 59th Annual Allerton Conference on Communication, Control, and Computing, 2023.
K. Paarporn. The madness of people: rational learning in feedback-evolving games. European Control Conference, 2024.
C. Hill, P. N. Brown, K. Paarporn. Conditions for Altruistic Perversity in Two-Strategy Population Games. American Control Conference, 2024.
R. C. Gavin, K. Paarporn, M. Cao. An Analysis of Logit Learning with the r-Lambert Function. IEEE Conference on Decision and Control, 2024.
R. C. Gavin, K. Paarporn, M. Ye, L. Zino, M. Cao. "Adaptive-Gain Control for Equilibrium Selection in the Logit Dynamics". IEEE Conference on Decision and Control, 2025.

Social behavior in epidemics

Infectious diseases pose a major risk to global health. They spread through physical contact between infected and susceptible individuals. These interactions can be modeled by a network whose edges describe the physical connections. Behavior also plays a role in how an epidemic runs its course. Individuals that receive information about the disease from social contacts or broadcasts can take preventative measures. Our work has analyzed dynamical models of epidemic spread over networks where the agents, or nodes in the network, are informed of disease prevalence through social contacts that may or may not coincide with their physical contacts. Based on their information, the agents take preventative measures by social distancing - reducing contact with their physical neighbors to lower the probability of getting infected.

K. Paarporn, C. Eksin, J.S. Weitz, and J. S. Shamma, Networked SIS Epidemics with Awareness, IEEE Transactions on Computational Social Systems, vol. 4, no. 3, pp. 93-103, Sept. 2017
C. Eksin, K. Paarporn, and J.S. Weitz. Systematic biases in disease forecasting - the role of behavior change . Epidemics, vol. 27, pp. 96-105, 2019 (Press release)
H. Khazaei, K. Paarporn, A. Garcia, and C. Eksin. Disease Spread Coupled with Evolutionary Social Distancing Dynamics Can Lead to Growing Oscillations. 60th IEEE Conference on Decision and Control, Austin, TX, 2021.
K. Paarporn, C. Eksin. SIS epidemics coupled with evolutionary social distancing dynamics. American Control Conference, pp. 4308-4313, 2023.

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