Mark Lovett

I am a Ph.D. candidate in applied mathematics at Dartmouth College, where I study game theory and artificial intelligence (AI). In my game-theory research I model social interactions by reducing complex networks to concise, tractable representations. These models help explain and predict phenomena across political science, AI, evolutionary ecology, and business. My game-theory work focuses on several related themes:

• Influence and allocation games — Models of competition for limited resources in which players exert influence to secure resources. Examples include political campaigns (candidates competing for votes) and platform competition for users; similar dynamics arise in machine-learning settings where agents compete for attention or data.
• Evolutionary game theory — The study of how strategies evolve in populations. By reducing complex population dynamics to tractable mathematical models, evolutionary game theory yields accurate predictions of agent behavior and has applications in ecology (for example, Tilman et al.), cooperation studies (for example, Traulsen et al.), decision making, and AI–human cooperation. (Tilman, Traulsen)

In AI, I study the incentive structures and learning environments that shape agent behavior — essentially, which “games” drive learning and why. I investigate how formally specified objectives influence learning dynamics and emergent strategies in both simulated and real-world settings. Much of my work focuses on applications of large language models and multi-agent reinforcement learning. Understanding the competitive and cooperative games in these settings helps develop smarter agents, more efficient learning, and improved interoperability.

Combining insights from AI and game theory has major applications for AI–AI, human–human, and especially human–AI interactions. Several open problems in AI cooperation can be studied within a game-theoretic framework (see, for example, this survey).

Beyond research, I have thoroughly enjoyed teaching at Dartmouth. I design scalable, technology-enabled course frameworks and curricula that improve delivery and student experience. My teaching emphasizes clear learning objectives, reproducible assignments, and practical tools that make courses easier to run and extend. I have five years of TA experience across 12 courses at two institutions, and I have independently taught one course.

Outside of academics, I am a senior member Amifore Consulting and Management LLC, a startup focused on delivering ethical consulting solutions to emerging businesses. Currently serving clients internationally, including in Switzerland, I specialize in AI and innovation consulting, helping organizations leverage cutting-edge tools to enhance operational efficiency and adapt to rapidly evolving technological landscapes. My work involves implementing strategic project management systems and demonstrating how AI-assisted workflows can optimize business structure and operational efficiency. This entrepreneurial experience has deepened my understanding of how AI research translates to real-world business applications and reinforced the critical importance of responsible AI deployment in emerging organizations.