Introduction
Claude Shannon gave us a precise sense in which information reduces uncertainty. This essay connects entropy and mutual information to hidden-state resolution in sequential tasks: each observation is a question put to the environment.
Bits, hazards, and safe policies
Information-theoretic quantities bound how much must be learned before a policy can be safe—not merely successful—when traps are statistically ambiguous until probed. DWMB-style benchmarks reward agents that acquire bits before stepping into irreversible loss.
Rate-distortion and abstraction
Rate-distortion theory explains why coarse world models can outperform fine ones under bounded computation: compression is not laziness but resource allocation. Shannon’s lens clarifies when “ignoring detail” is optimal versus when it courts disaster.
Statistical versus strategic concealment
Not all hiddenness is entropic. Adversaries hide structure strategically; markets hide information contractually. Shannon’s framework must pair with game theory when models face malicious partial observability.
Conclusion
Shannon’s legacy is not a reduction of meaning to bits but a discipline: quantify what you do not yet know, then design sensing and action to shrink that ignorance before the world charges interest.