STANDARDIZED REFERENCES WITH REVIEWS ... [ Word count: 8.600 ~ 35 PAGES | Revised: 2018.11.7 ]
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Therefore I thought:
[BAL13] : Each person has a mental effort budget. After learning some processing occurs in the brain, taking significant time. Else there is no remembering. No comprehending. bp > ix > gd > su > er > pt bp [ABE96] Harold ABELSON, Gerald SUSSMAN, Structure and interpretation of computer programs, Cambridge: Massachusetts Institute of Technology Press, 1996. bp [ABRA17] Samson ABRAMSKY, Contextuality: at the borders of paradox, Categories for the working philosopher, Oxford: University Press, 2017. bp [ABRA99] Samson ABRAMSKY, Guy MCCUSKER, Game semantics, Computational logic, Berlin: Springer, 1999. bp [ABRA09] Samson ABRAMSKY, Bob COECKE, Categorical quantum mechanics, Quantum logic, Amsterdam: Elsevier North Holland, 2009. bp [ADEL65] Irma ADELMAN, Long cycles, American economic review, 55(3):444--463, 6.1965. [ADDED] ix [AKER70] George AKERLOF, The market for lemons: Quality uncertainty and the market mechanism, Quarterly journal of economics, 84(3):488--500, 8.1970. 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I'm a scientist who writes fantasy and science fiction under various names. The magazines that I most recommend: Magazine of fantasy and science fiction, Compelling science fiction, Writers of the future, . . . #writing #creativity #science #fiction #novel #scifi #publishing #blog This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License . . . . . . . . . . . . . . . Text and images: ©tibra.
Therefore more can afford to read that which costs them less to read.
So what should be done? — Why not something which supports the concise use and reuse of each reference, much like that which supports the concise use and reuse of code? Open the text in one window. Then open the references in another window.
The nonrepeating letters in the review marks are mostly arbitrary. Rather they're only such that many typos must be made to accidentally produce a transition from an intended review mark to another, which makes it far less likely. Not so frequent.
Only a –2 is properly a bad review. Each –1 review is really a neutral review. Rather time reading has a cost: — therefore neutral reviews are negatives. Time reading is budgeted; this cost — the next best opportunity foregone — are the other things not read only because these things were read. — So everything 0, 1, 2, 3 is basically recommended.
⇅ ⇅ ⇅ ⇅ ⇅ ⇅
3 > 2 > 1 > 0 > –1 > –2
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bp [RIE81] Al RIES, Jack TROUT, Positioning, New York: McGraw Hill, 1981.NONFICTION: \section{Sa–Se}: 20
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@tribesteemup @thealliance @isleofwrite @freedomtribe @smg
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11/5 Added 48 references.
11/6 Added 2 references; some typos fixed.
11/7 Added 21 references.
Oh man.. lol! Dizzy 😂
PREVIEW. Considering the various utility tokens/cryptocurrency markets as "medium size markets". Where decisions of various participants are still significant strategically in deciding payoffs for actions of many other players. Many buyers and sellers are large enough relative the market to affect prices; meanwhile most pitches are technology pitches, basically. Different ones. And ones where features get added over time. So a systems would need need to predict the evolving value of many games with varying rules, each with unknown numbers of rounds and imprecise payoffs, considering many pitches are perfect substitutes for each other, allowing value to vary widely based on minor consumer preferences. Even if most are highly correlated most of the time, which is also partly for the same reason and partly because publicity and resources of the same developers and buyers and sellers are distributed over many competing pitches. (Value of a game is the average best possible payoff.)
One approach is that players don't know which game out of an infinity they are really playing. Players see payoffs, and games with some rules, different ones for different players, and make decisions based on that, but actually all play some one game with similar by continuous variation but not necessarily those same rules, and with payoffs that are the same only up to certain size random variations. Which affects the resources players have in later rounds then. Which changes which actions they have to choose from. Some players know better which games they are playing, and by their actual accumulated rewards and decisions signal that to other players.
Another approach is more abstract, but actually easier to match up predictions neural nets can easily perform.
There are several ways to relate this to mean reversion and pair trading strategies exist.
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