Optimising for Trouble – Game Theory and AI Safety with Jobst Heitzig

What happens when an AI system faithfully follows a flawed goal? In this episode, we explore how even well-designed algorithms can produce dangerous outcomes — from amplifying hate speech to mismanaging infrastructure — simply by optimising a reward function which, like all reward functions, fails to encode all that matters. We discuss the hidden risks of reinforcement learning, why over-optimisation can backfire, and how game theory helps us rethink what it means for AI to act "rationally" in complex, real-world environments. Jobst Heitzig is a mathematician at the Potsdam Institute for Climate Impact Research and an expert in AI safety and decision design.

You can listen to Optimising for Trouble – Game Theory and AI Safety with Jobst Heitzig online on Radio and Podcast. Open the player on this page to stream the available audio.

Optimising for Trouble – Game Theory and AI Safety with Jobst Heitzig is an episode from Game Changer - the game theory podcast by TWS Partners.

This episode is 26:45 long.

This episode was published on Feb 17, 2026.

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