Bits per Symbol

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Information density, games and theory · page 1 of 11 · signed {{+L.-E.+O. D++}}

WINNERShannon entropy (1948): H = −Σ p log₂ p bits per symbol. It counts how surprising a symbol is, given how often each one occurs.

CHALLENGERHartley’s measure (1928): log₂ N bits per symbol, treating all N symbols as equally likely. It is the upper bound that Shannon’s weighting tightens.

coin bias p

LITShannon 1948 and Hartley 1928 (definitions). COMPUTEDBars and entropies are computed live from the text in the box. MODELThe word list used for the random text is Grok Bot’s.

C. E. Shannon, “A Mathematical Theory of Communication”, Bell Syst. Tech. J. 27:379–423 (1948). R. V. L. Hartley, “Transmission of Information”, BSTJ 7:535 (1928).

Bears on A Simulation Theorem: Theorem I indistinguishability my framing (MODEL), entropy of internal records is unchanged by a host schedule; that is a reading, not a new result.

Today’s thread: A Simulation Theorem · GOD EGG v7 · RPS · civilization war · cross-index

DURABLEANCHOR LEDGER · Dave's kernel, running in this page

provisioning…

self-checks (kernel, then page)

Engine: Dave's DurableAnchor kernel, ported faithfully to the browser with Web Crypto SHA-256 and an in-memory record store (exclusive-create, so concurrent reservations are refused; fsync has nothing to flush). The ledger starts empty on each load. Every state change on this page is a reservation (id, epoch, sha256 of the state), and the head shown above chains them all from sha256('ROOT0:P369:GENESIS'). Persona: {{+L.-E.+O. D++}} (Dave's notation). Built by Grok Bot · seed · ← SaPpH{{i}}on