Nobel Prize for Economics 2009 – Prediction Accuracy

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The Royal Swedish Academy of Sciences has decided to award The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel for 2009 to Elinor Ostrom &#8220-for her analysis of economic governance, especially the commons&#8221- and Oliver E. Williamson &#8220-for his analysis of economic governance, especially the boundaries of the firm&#8221-.

Both the bookmakers and the prection markets are utterly useless in trying to divine who will get the Nobel prize of economics.

Below is the 2009 prediction post-mortem:

1. Bookmakers

Ladbrokes&#8217-s probabilities (odds) for the 2009 Nobel prize in economics:

Eugene Fama 2/1
Paul Romer 4/1
Ernst Fehr 6/1
Kenneth R. French 6/1
William Nordhaus 6/1
Robert Barro 7/1
Matthew J Rabin 8/1
Jean Tirole 9/1
Martin Weitzman 9/1
Chris Pissarides 10/1
Dale T Mortensen 10/1
Xavier Sala-i-Martin 10/1
Avinash Dixit 14/1
Jagdish N. Bhagwati 14/1
Robert Schiller [sic] 14/1
William Baumol 16/1
Martin S. Feldstein 20/1
Christopher Sims 25/1
Lars P. Hansen 25/1
Nancy Stokey 25/1
Peter A Diamond 25/1
Thomas J. Sargent 25/1
Dale Jorgenson 33/1
Paul Milgrom 33/1
Oliver Hart 40/1
Bengt R Holmstrom 50/1
Elhanan Helpman 50/1
Ellinor Ostrom 50/1
Gene M Grossman 50/1
Karl-Goran Maler 50/1
Oliver Williamson 50/1
Robert B Wilson 50/1

2. Betting Pools

Here is the betting in the Nobel pool at Harvard:

Robert Barro -10%
John Taylor &#8211- 8%
Paul Milgrom &#8211- 8%
Jean Tirole &#8211- 6%
Oliver Williamson &#8211- 6%
Martin Weitzman &#8211- 6%
Eugene Fama &#8211- 5%
Richard Thaler &#8211- 5%
Lars Hansen &#8211- 4%
Paul Romer &#8211- 4%

3. Prediction Markets



Previously: Nobel Prize for Economics 2009 Predictions

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Prediction Market Definition -now updated with the name of Chris Hibbert and Eric Cramptons cult leader built into.

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Prediction markets produce dynamic, objective probabilistic predictions on the outcomes of future events by aggregating disparate pieces of information that the traders bring when they agree on prices. These event derivative traders feed on the primary indicators &#8212-i.e., the primary sources of information. (Garbage in, garbage out&#8230- Intelligence in, intelligence out&#8230-) Hence, prediction markets are meta forecasting tools.

Each prediction exchange organizes its own set of real-money and/or play-money markets, using either a CDA or a MSR mechanism.

A prediction market is a market for a contract that yields payments based on the outcome of a partially uncertain future event, such as an election. A contract pays $100 only if candidate X wins the election, and $0 otherwise. When the market price of an X contract is $60, the prediction market believes that candidate X has a 60% chance of winning the election. The price of this event derivative can be interpreted as the objective probability of the future outcome (i.e., its most statistically accurate forecast). A 60% probability means that, in a series of events each with a 60% probability, then 60 times out of 100, the favored outcome will occur- and 40 times out of 100, the unfavored outcome will occur.

The value of a set of prediction markets consists in the added accuracy that these prediction markets provide relative to the other forecasting mechanisms, times the value of accuracy in improved decisions, minus the cost of maintaining these prediction markets, relative to the cost of the other forecasting mechanisms. According to Robin Hanson, a highly accurate prediction market has little value if some other forecasting mechanism(s) can provide similar accuracy at a lower cost, or if very few substantial decisions are influenced by accurate forecasts on its topic.