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.

My research has developed ``Colonel Blotto games" as a unifying framework to study various elements in competitive resource allocation problems. 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. Therefore, Colonel Blotto games are naturally suited for many different applications, including cybersecurity, economic competitions, social influence, and military strategy. 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", e.g. the performance improvement that is attainable from having some additional units of information.

A few relevant papers are listed below.

K. Paarporn, R. Chandan, M. Alizadeh, and J. R. Marden, Characterizing the interplay between information and strength in Blotto games, 58th IEEE Conference on Decision and Control, pp. 5977-5982, Nice, France, Dec. 2019
R. Chandan, K. Paarporn, and J. R. Marden. When showing your hand pays off: Announcing strategic intentions in Colonel Blotto games. American Control Conference, pp. 4632-4637, 2020
K. Paarporn, M. Alizadeh, and J.R. Marden. A risk-security tradeoff in graphical coordination games. IEEE Transactions on Automatic Control., vol. 66, no. 5, pp. 1973-1985, 2021
K. Paarporn, B. Canty, P. N. Brown, M. Alizadeh, and J.R. Marden. The impact of complex and informed adversarial behavior in graphical coordination games. IEEE Transactions on Control of Network Systems, vol. 8, no. 1, pp. 200-211, 2021.
K. Paarporn, R. Chandan, M. Alizadeh, and J. R. Marden. The Division of Assets in Multiagent Systems: A Case Study in Team Blotto Games. pp.1 663-1668, 60th IEEE Conference on Decision and Control, Austin, TX, 2021.
K. Paarporn, R. Chandan, M. Alizadeh, and J. R. Marden. A General Lotto game with asymmetric budget uncertainty. Submitted for journal publication, 2021.
K. Paarporn, R. Chandan, M. Alizadeh, and J. R. Marden. Asymmetric battlefield uncertainty in General Lotto games. IEEE Control Systems Letters, vol. 6, pg. 2822-2827, 2022.
K. Paarporn, R. Chandan, D. Kovenock, M. Alizadeh, and J. R. Marden. Strategically revealing intentions in General Lotto games. IEEE Transactions on Automatic Control (Early Access), 2024
A. Aghajan, K. Paarporn, and J. R. Marden. Extension theorems for General Lotto games with applications to network security. IEEE Transactions on Control for Network Systems (TCNS), vol 11, no. 1, pp. 185 - 196, 2024.

Control of large-scale multi-agent systems

Societal outcomes are driven by the decision-making of large populations of agents, e.g. the impacts of climate change, the spread of infectious diseases, the security of cyber-space, and the state of natural ecosystems. 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 of my research 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 in the context of evolutionary game theory. Our work examines 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. Our insights provide a new avenue towards the control of sustainable human-resource systems.

J.S. Weitz, C. Eksin, K. Paarporn, S.P. Brown, and W.C. Ratcliff, An oscillating tragedy of the commons in replicator dynamics with game-environment feedback, Proceedings of the National Academy of Sciences, 113(47):E7518–E7525, 2016 (Press release)
K. Paarporn, C. Eksin, J.S. Weitz, and Y. Wardi, Optimal control policies for evolutionary dynamics with environmental feedback, 57th IEEE Conference on Decision and Control, Miami, FL, 2018
H. Khazaei, K. Paarporn, A. Garcia, and C. Eksin. Disease Spread Coupled with Evolutionary Social Distancing Dynamics Can Lead to Growing Oscillations. pp. 4280-4286, 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.
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 (to appear), 2024.
K. Paarporn, J. Nelson. Two competing populations with a common environmental resource. IEEE Conference on Decision and Control (to appear), 2024.

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Influence of networked multi-agent systems

The interactions of multiple, independent decision-makers forms the core of a networked multi-agent system. While direct control of these systems, such as social networks, smart grids, and transportation networks is not possible, influence mechanisms can be applied at the individual level. This influence can be magnified by cascading effects through a network.

My research has focused on identifying influence policies, e.g. algorithms, that can be applied to a networked system such that its emergent behavior is desirable. Our work has identified trade-offs in the design of local decision-making rules that can mitigate the effects of adversarial influences, but subjects the system to inefficiencies. The role of information is also central to this analysis. How does an adversary's knowledge (or lack thereof) about the network impact its ability to spread its influence through the network?

C. Eksin, K. Paarporn, Control of learning in anti-coordination network games. IEEE Transactions on Control of Network Systems, vol. 7, no. 4, pp. 1823-1835, 2020.
K. Paarporn, M. Alizadeh, and J.R. Marden. A risk-security tradeoff in graphical coordination games. IEEE Transactions on Automatic Control., vol. 66, no. 5, pp. 1973-1985, 2021
K. Paarporn, B. Canty, P. N. Brown, M. Alizadeh, and J.R. Marden. The impact of complex and informed adversarial behavior in graphical coordination games. IEEE Transactions on Control of Network Systems, vol. 8, no. 1, pp. 200-211, 2021.

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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