Good Thinking: The Foundations of Probability and Its Applications

by Irving John Good

Good Thinking was first published in 1983.Good Thinking is a representative sampling of I. J. Good’s writing on a wide range of questions about the foundations of statistical inference, especially where induction intersects with philosophy. Good believes that clear reasoning about many important practical and philosophical questions is impossible except in terms of probability. This book collects from various published sources 23 of Good’s articles with an emphasis on more philosophical than mathematical.He covers such topics as rational decisions, randomness, operational research, measurement of knowledge, mathematical discovery, artificial intelligence, cognitive psychology, chess, and the nature of probability itself. In spite of the wide variety of topics covered, Good Thinking is based on a unified philosophy which makes it more than the sum of its parts. The papers are organized into five Bayesian Rationality; Probability; Corroboration, Hypothesis Testing, and Simplicity; Information and Surprise; and Causality and Explanation. The numerous references, an extensive index, and a bibliography guide the reader to related modern and historic literature.This collection makes available to a wide audience, for the first time, the most accessible work of a very creative thinker. Philosophers of science, mathematicians, scientists, and, in Good’s words, anyone who wants “to understand understanding, to reason about reasoning, to explain explanation, to think about thought, and to decide how to decide” will find Good Thinking a stimulating and provocative look at probability.

Genres: Science, Philosophy, Mathematics

332 pages, Hardcover

First published December 1, 1983

Reviews

[Gwern · Rating 2 out of 5]

Moved to gwern.net.

[Nils Lid Hjort · Rating 5 out of 5]

Den uforlignelige I.J. Good, venn og medforfatter av den fiktive J.I. Doog den gang han ikke fikk lov til å skrive "we" i en artikkel han hadde skrevet, tilhører de ytterst interessante, originale, fritttenkende probabilister, statistikere, maskinlærere, datavitenskapspersoner, Bayesianere, tja, kanskje man skal føye til "tallknusere" også.

Han vant annen verdenskrig (vel, sammen med Alan Turing); han har funnet på den moderne datamaskin; han har forutsett og satt navn på og problematisert The Singularity; han har skrevet en artikkel om trykkfeil i egne publikasjoner; han har beregnet en lower bound for the number of different Bayesian statisticians (det er, eller burde være, minst 46656 ulike Baeysianere); han har slitt ut ti sekretærer og muligens fridd til den ellevte; han har skrevet The Botryology of Botryology; &c., &c.

Dette er altså en samling av hans vidtfavnende og bredspektrede artikler.

Les også David Banks' intervju med ham i Statistical Science, 1996. Jeg må maile ham og spørre om en bestemt Good-referanse jeg ikke fant frem idag.

PS: Kanskje I.J. Good ville likt den følgende idiosynkratiske selvutleverende fotnote: Jeg kikket altså på denne boken, på Goodreads, og fomlet en anelse med mine fingre, mens jeg så etter mulige reviews. Plutselig & uplanlagt og vips-vops hadde GR klassifisert meg som "Wanting to read" denne boken. Jeg finner ingen av-knapp på GR, der jeg kunne ha skyndet meg å undo dette. Jeg vil i grunnen ikke ha noen "want to read"-bøker her, og dessuten vil jeg si noe om hver bok jeg merker av. Antallet bøker under MyBooks skal presist matche antallet bøker jeg har lagt inn et review om. Altså måtte jeg, uforvarende & unplanlagt, forfatte dette review, i den proverbiale fulle fart. For jeg har altså lest boken!

[Daniel · Rating 4 out of 5]

This collection of scientific papers is a challenging but useful discussion on statistical methods, probability, randomness, logic and decision-making. Much of the book centers around Bayesian statistical methods and when and why to use them, as well as "philosophy of science"-type discussions on when a scientist should--or sometimes must--apply subjective judgments to scientific problems.

It will help enormously if you've had a semester or two of statistics to really get at the meat of this book. If not, scroll down a few... [see the rest on my book review site.]