Combinatorial Prediction Markets
author:
Robin Hanson,
George Mason University
Description
Several hundred organizations are now using prediction markets to forecast sales, project completion dates, and more. This number has been doubling annually for several years. Most, however, are simple prediction markets, with one market per number forecast, and several traders per market. In contrast, a single combinatorial prediction market lets a few traders manage an entire combinatorial space of forecasts. For millions of numbers or less, implementation is easy, and lab experiments have confirmed feasibility and accuracy. For larger spaces, however, many open computational problems remain.
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| Slides | |
| 0:00 | Combinatorial Prediction Markets |
| 0:11 | What Do “We” Believe? |
| 3:05 | Buy Low, Sell High |
| 5:38 | Today’s Current Event Prices |
| 6:54 | Beats Alternatives |
| 8:09 | Prediction Market Accuracy in the Long Run |
| 8:34 | Policy Analysis Market |
| 9:04 | The Fuss |
| 10:28 | Companies that Have Implemented an Internal Prediction Market |
| 10:55 | Internal Applications |
| 11:19 | Prediction Markets |
| 12:07 | Not Experts vs. Self-Chosen Amateurs |
| 13:12 | Advantages |
| 13:52 | Ad Agency Decision Markets |
| 16:04 | Corporate Applications |
| 16:48 | Decision Market Requirements |
| 16:56 | - Questions |
| 20:02 | - Questions |
| 21:10 | Combo Betting |
| 24:39 | Sport Finals Tickets |
| 26:07 | PAM Scenario |
| 27:37 | Some Consensus Mechanisms |
| 28:26 | Old Tech Meets New |
| 29:44 | Opinion Pool “Impossibile” |
| 30:01 | Best of Both |
| 34:35 | Quantal Response Modularity |
| 34:53 | Laboratory Tests |
| 35:30 | Environments: Goals, Training |
| 37:42 | Experiment Environment |
| 38:06 | MSR Info vs. Time – 3 Variables |
| 38:32 | MSR Info vs. Time – 255 Prices |
| 38:43 | Combinatorial Lab Experiments |
| 39:21 | Combo Market Maker Best of 5 Mechs |
| 39:31 | Compute Tasks |
| 44:54 | - Questions |
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