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Dipen Shah4d ago · 8 min read

The Fortress of Geography: How Spatial Networks and 2D Grids Shield Cooperation from Parasites

Opening question In Episodes 1 through 4, our computational society operated under a fundamental assumption: The well-mixed soup. Every agent was assumed to have an equal probability of bumping into a

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Nick One-DropOct 7 · 3 min read

Multi-agent cooperation through in-context co-player inference (2026)

This paper takes a decentralized multi-agent reinforcement learning approach. What does that mean here? For context, let’s look at another DeepMind paper with overlapping authors: Multi-agent cooperat

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Dipen ShahOct 4 · 12 min read

Simulating Natural Selection: Why Unconditional Kindness Dies, and How Clusters of Trust Conquer the World

Opening question In Episodes 1 through 3, our computational tournaments operated under a fixed social contract: The population was static. The experimenter handpicked the participants, and every arche

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Dipen ShahSep 27 · 12 min read

Simulating Noise and Forgiveness: Why Pure Reciprocity Collapses When Communication Breaks

Opening question In Episodes 1 and 2, we proved that reciprocal trust can survive against raw exploitation and build an interconnected society. Under round-robin tournament conditions, Robert Axelrod'

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Dipen ShahSep 20 · 5 min read

Building a Multi-Agent Tournament Engine: Simulating Emergent Cooperation in Python

In Episode 1: Building the Laboratory, we constructed a deterministic, isolated foundation for repeated two-agent Prisoner's Dilemma interactions. We verified that when interactions repeat, reciprocal

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Dipen ShahAug 25 · 7 min read

I Built a Simulation to Explore Why Selfish Agents Cooperate

Building the domain foundation for an open-source simulation of cooperation, defection, and emergent behaviour. What happens when self-interested agents interact with each other again and again? Do t

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SangharshaAug 24 · 3 min read

Deceptive AI Doesn’t Break the Rules. It Optimizes Around Them: with game theory

Everyone assumes deception in AI will look obvious — a system glitch, a sudden compute spike, or a glaring anomaly. That assumption is fundamentally wrong. The most effective deception doesn’t violate

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OmnithiumMay 31 · 9 min read

Multi-Agent Negotiation Protocols: How AI Agents Should Bargain for Resources

Static quotas are the death of agentic autonomy. If you're still using Kubernetes-style resource limits to manage your AI swarms, you're likely leaving 30% to 40% of your compute capacity on the table

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Macaulay001May 11 · 7 min read

I reran Axelrod's 1981 prisoner's dilemma tournament. Tit-for-tat still earns its reputation.

Part 49 of Forgotten Theories, a series re-testing old scientific claims with modern tools. Find the rest under the #forgotten-theories tag. In 1980, the political scientist Robert Axelrod sent letters to game theorists, economists, psychologists, a...

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Berkan SesenMay 11 · 16 min read

Q-Learning for Games: Teaching an Agent Tic-Tac-Toe Through Self-Play

Tic-tac-toe is a solved game. Any competent adult can force a draw every time. But can an agent figure that out with zero human knowledge? Give two agents a blank board, a few simple rules about wins

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The Fortress of Geography: How Spatial Networks and 2D Grids Shield Cooperation from Parasites

Multi-agent cooperation through in-context co-player inference (2026)

Simulating Natural Selection: Why Unconditional Kindness Dies, and How Clusters of Trust Conquer the World

Simulating Noise and Forgiveness: Why Pure Reciprocity Collapses When Communication Breaks

Building a Multi-Agent Tournament Engine: Simulating Emergent Cooperation in Python

I Built a Simulation to Explore Why Selfish Agents Cooperate

Deceptive AI Doesn’t Break the Rules. It Optimizes Around Them: with game theory

Multi-Agent Negotiation Protocols: How AI Agents Should Bargain for Resources

I reran Axelrod's 1981 prisoner's dilemma tournament. Tit-for-tat still earns its reputation.

Q-Learning for Games: Teaching an Agent Tic-Tac-Toe Through Self-Play