Week 0: When AI meets humanity.

Hey everyone! My name’s Rohan Saketh Pilli.

A little bit about myself: My interests span far and wide, from the rigors of Real Analysis to the abstraction of Multivariable Calculus, from the applications of computer science to thought experiments of philosophy, and from the study of human behavior through martial arts to social interaction through teaching.

All of these endeavors have provided me a spark of inspiration, a curious thought that combines all of these principles together. Can we simulate human behavior?

Daunting task, definitely, so I’ve decided to focus on a couple key factors: Altruism, and Future Planning.

So… here’s where I’m headed. I’ve been mulling over what it really means to incorporate altruism and planning into a simulation. Currently, my plan is to build a digital sandbox where agents operate based on a very streamlined set of rules—almost like a digital petri dish for behavior. Initially, these agents will follow simple instructions: some will be programmed to seek personal gain, while others might have a built-in bias to help neighboring agents. The fascinating part will be watching how these simple directives interact over time.

In theory, even these elementary rules could lead to surprisingly complex outcomes. For example, when an agent has to decide between maximizing its own reward or sacrificing a bit of that reward to benefit the collective, emergent patterns of altruism might begin to appear. At the same time, incorporating elements of planning will let these agents not just react in the moment, but anticipate future outcomes—forcing them to weigh short-term gains against long-term benefits. I’m particularly excited about the possibility that, through repeated interactions, the simulation might reveal the very building blocks of human-like decision-making.

Over the next few weeks, I plan to take a deep dive into agent-based modeling techniques, drawing inspiration from both cellular automata and modern reinforcement learning frameworks. By tweaking parameters like reward structures, planning horizons, and even the connectivity between agents, I hope to observe clear transitions: moments when behavior shifts from purely self-interested to something that more closely resembles collaborative planning. Essentially, I want to uncover how complex, sometimes unpredictable behavior can emerge from a few simple, well-defined rules.

Can the complex net of human decisions be reduced to a set of simple algorithms? And if so, what does that say about the nature of intelligence itself? These are the kinds of questions that keep me up at night, which I hope to quell (or make worse) through this project.

Even at this early stage, the potential for discovery is immense. I’m excited to share every step of this journey with you as I move from theoretical blueprints to practical experiments, and hopefully, from simple rule sets to a glimpse of emergent behavior that echoes the complexity of real life.

Stay tuned for more updates as I experiment with these ideas! The next step is to make a basic simulation without any values, just the framework. Excited to update with my progress with my project, “Simulating Natural Selection through AI Agents of Neural Network-based Intelligence honed through Evolutionary AI Algorithm”!