Author Topic: Thoughts on Prediction-Markets (Brain Analogy)  (Read 2897 times)

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Offline CWEvans

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Huffington Post ran an article in 2009 that summed up the problem with the public's understanding of economics— "
Priceless: How The Federal Reserve Bought The Economics Profession
"—that describes how the Federal Reserve dominates the economics profession in the USA.

Considering that so many tacitly accept that logical fallacies like Appeal to Authority, Appeal to Popular Sentiment, and Appeal to Tradition are the foundation of legislative and judicial systems worldwide, it is not so surprising that a special interest group like the Federal Reserve might hold sway by coopting elite professors at elite universities.

When one questions the received wisdom that mild deflation is worse than mild inflation, it is not uncommon to be on the receiving end of Ad Hominem  that is synonymous with, "Every schoolboy knows...!"

Offline bytemaster

Yes, I have encountered this dangerous mindset.  It comes from the mentality that one persons gain is another persons loss instead of realizing the truth that all voluntary trades are gains for both parties.   

Those that attempt to eliminate 'profit' merely move the profit to someone else.  Those who claim you should be selfless have selfish motives for making such a demand.  After all, someone other than me gains from my selfless act... isn't that rather, selfish of them?    Thus all charity should be done for the joy of the giver (selfish) and not for the benefit of the receiver (selfless).   

When it comes to hoarding people have an irrational fear of deflation promoted by banks which have created a ponzi scheme that will unravel with deflation.   Hoarding is just demand.  It saves resources for the future when they are needed.  The opposite of hoarding is consumption.   The argument against hoarding is an argument against savings which is an argument against capital accumulation necessary to build the infrastructure of society required to increase abundance.
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Offline luckybit

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Brilliant analogy and quite possibly an indication that inelegance in the brain may be driven by decentralized market forces as all the cells in the body work together to maximize their lives.   DNA is thus the block-chain of life so to speak.  It defines what transactions and economic conditions a life form must follow.

Is it possible that low level cells are making simple economic choices? 

The biggest challenge with creating a global brain is the human-computer interface.  We become like cells that must take the time and effort to communicate and participate in every market.   Creating a market that has no participants does not work.

I happen to think you're correct but can you explain to me why so many people prefer non-profit motives over for profit or why so many people make the argument that deflation is bad and that hoarding (saving?) is bad?

I fail to see how anything can survive in a sustainable way without being selfish. So if we remove selfishness from an organization (such as with non-profits) or from the design of the currency itself (such as if it loses value over time), how exactly is it supposed to sustain?

I'm just trying to compare and contrast these two different arguments because there are factions who are saying the exact opposite of what you propose is necessary and that Bitcoin by design is too selfish.

http://ouishare.net/2013/05/bitcoin-human-based-digital-currency/
http://yanisvaroufakis.eu/2013/04/22/bitcoin-and-the-dangerous-fantasy-of-apolitical-money/

Maybe I can see their argument in the long term. 100-200 years from now when we are all dead and / or technology is so sophisticated that we are cyborgs who don't need money of this kind anymore due to a technological singularity producing a state of post-scarcity, but for our lifetimes (or at least the next 40-50 years) I don't see how we will get from point A to point B without inherent selfish motives involved.

What are your thoughts on their approach? I'm sure you know what I'm talking about and have encountered this faction.


« Last Edit: February 12, 2014, 02:17:20 pm by luckybit »
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Offline CLains

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To what extent is it possible to generalize this to collect any empirical information? I've only seen this thread on the matter so far.

A Sensor DAC could work so long as their was a prediction market attached to each sensor that predicted the reliability of that sensor.  The sensor would publish a signed data feed and the market would price the trustworthiness of that feed.

Anyone with a competing Sensor could then have an automated trading bot that attempts to gain first-mover advantage by shorting other sensors that disagree with it.   

Allow anyone to setup automated trading bots that attempt to make money detecting bad sensors and the DAC would have a valid, trustworthy set of data feeds.

Great! That's.. will work for any set of data feeds. :)

It will work for active data feeds where users query for information.
It will work for active and passive data feeds about confidential information.
..
..

A source of information combined with pricing of trustworthiness!

Offline bytemaster

A prediction-market may be the best way to get accurate empirical data into a DAC. With a prediction-market along the lines of Bitshares X, the DACs suddenly have sensation, the first step towards developing a full-fledged brain.

This pegging can be viewed as an empirical input-stream into the DAC. As a price-feed you have a sequence of numbers over time. These numbers can be understood as "rising," and "falling." Then you have the "acceleration" of these rises and falls. With these modest classifications you can already use pattern-recognition to find further, complex units of price-movements. Perhaps one can find that "crash" and "correction" can be identified and thus with some probability predicted.

How to make a DAC that collects accurate empirical data is one of the most fundemental things we should be thinking about. With Bitshares X this problem is solved for special cases. To what extent is it possible to generalize this to collect any empirical information? I've only seen this thread on the matter so far.

A Sensor DAC could work so long as their was a prediction market attached to each sensor that predicted the reliability of that sensor.  The sensor would publish a signed data feed and the market would price the trustworthiness of that feed.

Anyone with a competing Sensor could then have an automated trading bot that attempts to gain first-mover advantage by shorting other sensors that disagree with it.   

Allow anyone to setup automated trading bots that attempt to make money detecting bad sensors and the DAC would have a valid, trustworthy set of data feeds.

For the latest updates checkout my blog: http://bytemaster.bitshares.org
Anything said on these forums does not constitute an intent to create a legal obligation or contract between myself and anyone else.   These are merely my opinions and I reserve the right to change them at any time.

Offline CLains

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A prediction-market may be the best way to get accurate empirical data into a DAC. With a prediction-market along the lines of Bitshares X, the DACs suddenly have sensation, the first step towards developing a full-fledged brain.

This pegging can be viewed as an empirical input-stream into the DAC. As a price-feed you have a sequence of numbers over time. These numbers can be understood as "rising," and "falling." Then you have the "acceleration" of these rises and falls. With these modest classifications you can already use pattern-recognition to find further, complex units of price-movements. Perhaps one can find that "crash" and "correction" can be identified and thus with some probability predicted.

How to make a DAC that collects accurate empirical data is one of the most fundemental things we should be thinking about. With Bitshares X this problem is solved for special cases. To what extent is it possible to generalize this to collect any empirical information? I've only seen this thread on the matter so far.
« Last Edit: February 11, 2014, 02:25:53 pm by CLains »

Offline CLains

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It is clear that humans would need to be involved in this global brain, and as such there would be several human limitations. I am not sure what kinds of hierarchical prediction-structures with human components would be effective. It is possible to view cells as making simple economic choices I think, but am not sure how hierarchical structures in the brain - or any organic structures - are produced at genesis, so I have no good inspiration here!

I know that hierarchical structures are particularly valuable because they maximize the ability to perceive distinction while preserving unity. The key to perceived beauty is the apparent complexity combined with underlying unity. We need to be able to differentiate a billion different sentences or pictures, while having the ability to integrate each particular sentence or picture into a unitary whole.

Offline bytemaster

Brilliant analogy and quite possibly an indication that inelegance in the brain may be driven by decentralized market forces as all the cells in the body work together to maximize their lives.   DNA is thus the block-chain of life so to speak.  It defines what transactions and economic conditions a life form must follow.

Is it possible that low level cells are making simple economic choices? 

The biggest challenge with creating a global brain is the human-computer interface.  We become like cells that must take the time and effort to communicate and participate in every market.   Creating a market that has no participants does not work.



For the latest updates checkout my blog: http://bytemaster.bitshares.org
Anything said on these forums does not constitute an intent to create a legal obligation or contract between myself and anyone else.   These are merely my opinions and I reserve the right to change them at any time.

Offline CLains

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How does the brain Predict?

The brain takes input through channels called senses. These senses convert the various inputs, photons, pressure, molecules, etc. into electrical signals that are hierarchically processed. At the bottom of the hierarchy are simple spatial and temporal patterns of these electrical signals that correspond to the simplest "letters" in the language of that particular sensory modality. In visual (written) language we start out with simple lines and curves that form into letters, and letters form into morphemes and these form words and words form clauses and sentences and so on.

Since the brain processes the stimuli simultaneously on all levels of the hierarchy the brain already predicts, already knows how to complete each part of the sentence as it is being read and so "it deosn't mttaer in waht oredr the ltteers in a wrod are, the olny iprmoetnt tihng is taht the frist and lsat ltteer be at the rghit pclae" - the overall meaning of each word and the sentence overrides the local errors. The same principle underlies all of perception; sight, hearing, touch, etc.

This suggest that the main thrust of the powerful pattern recognition of the brain can be emulated in a DAC with a prediction-market. The key is to use specialized low-level predictions as input to higher-level predictions.


Offline CLains

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Perceptrons or Why Does the Brain Predict?

The brain is mainly a prediction-machine. It takes sensory input and through the process of perception figures out what sensory input will occur next. It does this by compressing sensory input in both time and space; if you see an occluded figure, you'll typically still recognize it. If you hear part of a song, you'll typically still recognize it. The brain has compressed the total figure and song, and will be able to predict its presence based on any fraction of the total information.

Why does the brain predict? To make useful actions possible. All moving creatures need to predict the consequences of both sensory input and the result of their own actions (output). This is why brains exist - to coordinate perception with action. At first, nervous systems were used to react immediately to stimuli, but over time the reactions grew to become thoughtful. At heart, the ability to predict the affordances of stimuli and the consequences of action are fundamental.

How do our run-of-the-mill IQ tests try to assess our intelligence? "Which number comes next?" "Pick the figure that completes the pattern," etc. Here we typically expect someone to complete a pattern on the basis of some limited sensory experience by means of finding an elegant way of compressing the given input where the unknown comes out simply.

There are also lots of patterns that are irrational in the sense that they cannot be compressed elegantly to reveal the unknown. To predict events in the world we typically mix these two forces: On the one hand we look for elegant, beautiful ways to compress the total information we are given. On the other hand, we are collecting a vast store of known patterns that exist in the world even if they are ugly (not easily compressible).

What can we draw from all this? First, prediction-markets should be seen as trying to complete incomplete knowledge in two senses, rationally and empirically, and never just temporally. Second, we should recognize that "incomplete knowledge" is a subjective feature that apply to limited perspectives, whether the limit be in intellectual power e.g. we can't compute the answer, scientific insight e.g. we don't know if it's empirically possible, or empirical knowledge e.g. we don't know if a particular fact is true.

I also want to mention that Perceptrons can take hypotheticals as input; IF x, then ?. This is what happens when Perceptrons are used to predict the consequences of the predictors action, it puts the given action as a hypothetical x, and then tries to predict what will happen. In this way we get the additional result that prediction-market also provides predictions about hypothetical situations.
« Last Edit: February 10, 2014, 11:27:24 pm by CLains »