Spigit Idea / Prediction Market Technology (as sent to me by Hutch Carpenter):
Prediction Market
Spigit implements traditional prediction markets, a tool for determining likelihood of future events and trends. In this model, market creator asks a question relating to a future event and multiple mutually exclusive answers that cover all possible outcomes. The creator also sets initial probabilities or prices. Spigit provides a simple trading model in which all transactions are executed between system’s market maker and the end user. Administrators can configure share buckets that users can choose to buy. The price is determined by a configurable price function; possibilities include quadratic, exponential, and linear. Irrespective of the actual function used, the price goes up as more shares are bought and goes down with shares sold. Users can set alerts when the stock price goes above or below user-specified values. When the market is closed, investors in the chosen answer receive 100 virtual dollars per share. All other investors lose their money.
Betting Market
This market inherits its basic structure from traditional prediction markets. Each market consists of a question and mutually exclusive answers relating to a future event, however, it does not involve stock market-like trading. Each user is given a fixed amount of tokens/chips for each open market. Users are free to distribute their tokens among possible answers as they see fit. They can change their bets at any time. Each market is assigned a budget by the market creator. When the market closes, the budgeted amount is uniformly distributed on per-token basis to all users that have placed their tokens on the accepted answer. This form of the market has several advantages over traditional prediction markets. It’s simpler to use since users do not have to deal with stock market formalism. Late entrants in the market benefit equally well when they bet their tokens on the right answer. The markets can be configured to allow users to play “blind†(i.e. not know what the current probabilities are when they place their bets), resulting in removing mob-effect bias.
Global Trends and Patterns
Activity Indexes–Indexes can be charted over a period of time to detect trends in participation levels.
Market price charts showing trends over different aggregation intervals and periods.
Trading volume – Overall and market-specific trading volume tracked hourly, daily, weekly, monthly.
Drill-Downs
Group Predictions – Market predictions made by user groups identified based on user attributes (location, position, age groups), etc., roles (executives, experts, etc.).
Reputation Weighted Predictions – If full spigit platform is utilized, predictions can be computed based on topic-specific reputations
Group Interests – Trading activity levels across active markets based on user roles and attributes
Bias Detection and Noise Removal
Two types of biases exist in any rating/betting system: proclivity of some individuals to over/under-rate an answer, and the tendency to favor outcomes that are beneficial to the individual or a group. The second one is applicable and discoverable for prediction markets. Spigit can compute prediction deviations for a group (based on user attributes/roles).
Spigit can reduce bias and the impact of day trading by computing probabilities after excluding trades made by members of the group that exceeds a configurable threshold.
