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Prediction Markets

This page was written by SamRose after our BarCamp:BarCampBankFlashMeeting5. FredericBaud moved it here to create a dedicated page to the subject.

Page maintainers: SamRose, FredericBaud

Introduction

There is a difference between WisdomOfCrowds and CollectiveIntelligence. I discuss this difference at P2P Foundation blog.

The "WisdomOfCrowds" as defined by James Suroweicki, in his book by the same name, has these contexts:

There are four key qualities that make a crowd smart. It needs to be diverse, so that people are bringing different pieces of information to the table. It needs to be decentralized, so that no one at the top is dictating the crowd’s answer. It needs a way of summarizing people’s opinions into one collective verdict. And the people in the crowd need to be independent, so that they pay attention mostly to their own information, and not worrying about what everyone around them thinks.

The need for independence among “crowd” members contrasts with the requirement for connection and collaboration to see collective intelligence work. This distinction is actually important for all Collective Problem Solving issues. MIT's Jenkins writes:

The Wisdom of Crowds model focuses on (aggregating) isolated inputs: the Collective Intelligence model focuses on the process of knowledge production.

So, our participation in transparent online forums, message boards, wikis, is a way to harness our "CollectiveIntelligence". This is an element that we should definitely keep. I am not advocating getting rid of participation in wikis.

But, sometimes CollectiveIntelligence can fail. Here's a quote from a page I wrote on another wiki about PredictionMarkets read quoted page here:

Purpose of Predictionmarkets

The purpose of prediction markets is to tap into the aggregated Wikipedia:{Wisdom of crowds}.

Wikipedia:{James Surowiecki}'s book by the same name lists the following four elements needed to form crowd wisdom:

(quoted from Wikipedia:{Wisdom of crowds}):

Four elements required to form a wise crowd

Not all crowds (groups) are wise. Consider, for example, mobs or crazed investors in a stock market bubble. Refer to Failures of crowd intelligence (below) for more examples of unwise crowds. According to Surowiecki, these key criteria separate wise crowds from irrational ones:

• Diversity of opinion Each person should have private information even if it's just an eccentric interpretation of the known facts.
• Independence People's opinions aren't determined by the opinions of those around them.
• Decentralization People are able to specialize and draw on local knowledge.
• Aggregation Some mechanism exists for turning private judgments into a collective decision.

Failures of crowd intelligence

Surowiecki studies situations (such as rational bubbles) in which the crowd produces very bad judgment, and argues that in these types of situations their cognition or cooperation failed because (in one way or another) the members of the crowd were too conscious of the opinions of others and began to emulate each other and conform rather than think differently. Although he gives experimental details of crowds collectively swayed by a persuasive speaker, he says that the main reason that groups of people intellectually conform is that the system for making decisions has a systematic flaw.

Surowiecki asserts that what happens when the decision making environment is not set up to accept the crowd, is that the benefits of individual judgments and private information are lost, and that the crowd can only do as well as its smartest member, rather than perform better (as he shows is otherwise possible). Detailed case histories of such failures include:

• Too centralized The Columbia shuttle disaster, which he blames on a hierarchical NASA management bureaucracy that was totally closed to the wisdom of low-level engineers.
• Too divided The U.S. Intelligence community failed to prevent the September 11, 2001 attacks partly because information held by one subdivision was not accessible by another. Surowiecki's argument is that crowds (of intelligence analysts in this case) work best when they choose for themselves what to work on and what information they need. (He cites the SARS-virus isolation as an example in which the free flow of data enabled laboratories around the world to coordinate research without a central point of control.)
• Too imitative Where choices are visible and made in sequence, an "information cascade" can form in which only the first few decision makers gain anything by contemplating the choices available: once this has happened it is more efficient for everyone else to simply copy those around them.

(end Wikipedia:{Wisdom of crowds} quote material released under GNU Free Documentation License)

PredictionMarkets tap into crowd wisdom by placing the incentive for correct decision making with each individual. PredictionMarkets work best when a knowledge about a problem is widely dispersed among many people.

PredictionMarkets do not perform well when all of the knowledge about a problem or its possible outcomes rests with just one person. (Example: when the outcome of a situation is based upon the decision of one person. Crowd wisdom is usually not any more effective at guessing what the decision of that individual will be than the indivual guess of experts, or even non-experts).

''PredictionMarket trading can be done with real currency, AlternativeCurrency (like a community currency that holds real value in certain locales), or with “play” money that has a totally imaginary value. Relevance To Problem Solving''

A PredictionMarket can be used to inform collective problem solving efforts in a way that potentially reduces the some of the negative or couner-productive aspects of group deliberating around problems. ome of the known problems with deliberation can be:

• AmplificationOfErrors
• People being influenced by information (InformationalInfluences)
• SocialPressures
• Group polarization and InformationCascade effects on group polarization
• Biases: Egocentric, “Hindsight”, Familiarity, Saliency, Cultural, and other biases
• CommonKnowledgeEffect, which refers to the observations that information held by most or all group members has far more influence than HiddenProfiles of information held by only a few members.

(The above six points are aggregated from CassSunstein ’s book, Infotopia: How Many Minds Produce Knowledge).

PredictionMarket aggregation of knowledge can potentially avoid some or all of these problems by focusing people on an individual incentive that does not requiredeliberation.

So, employing a PredictionMarket is one way to aggregate isolated inputs from individuals.

In the case of P2PVenture, PredictionMarkets can be used to advise decision making in the ProjectScreening process.

Many companies are employing prediction markets, examples include Google, IBM, Yahoo, and many, many others. Most of them are using a platform known as Inkling.

However, there is also an OpenSource PredictionMarket system that has virtually all fo the same functionality. That system is idea futures. A working version of it exists here. (you can login with username and password of "user1").

I've also set this software up on my own server here: http://socialsynergyweb.net/if/Trade

Still working out some of it's functionality.

I propose that we employ prediction markets in P2PVenture processes. If you are interested in using http://socialsynergyweb.net/if/Trade to experiment with my proposed idea, please let me know and I'll get it working and give you access. -- SamRose

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