Crowdsourced Outcome Determination in Prediction Markets
Authors: Rupert Freeman, Sebastien Lahaie, David Pennock
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we propose a specific prediction market mechanism with crowdsourced outcome determination that addresses several challenges faced by decentralized markets of this sort. Additionally, by allowing anyone to create a market, there is no longer any guarantee that all questions will be sensible, or even have a well-defined outcome. Our analysis will assume that μ1 and μ0 are common across agents, but this is not a strict requirement. If we allow each agent to have distinct updates μi 1, μi 0, then we can let μ1 = mini μi 1, corresponding to the minimum update given ˆxi = 1, and similarly μ0 = maxi μi 0. In this section, we derive conditions for truthful reporting (ˆxi = xi) to be a best response, given that all other arbiters report truthfully. We plot (3) and (4) in Figure 2, considering their tight versions as equalities. This paper proposed and analyzed a mechanism where the outcome of an MSR prediction market is determined via a peer prediction mechanism among a set of arbiters. |
| Researcher Affiliation | Collaboration | Rupert Freeman Duke University rupert@cs.duke.edu S ebastien Lahaie Microsoft Research slahaie@microsoft.com David M. Pennock Microsoft Research dpennock@microsoft.com |
| Pseudocode | Yes | Figure 1: Prediction market with outcome determined using peer prediction. 1. Market stage. (a) A prediction market is set up for an event X using a market scoring rule. (b) Agents trade in the market. For a security bought at price p, a trading fee of fp is charged, and for a security sold at price p, a fee of f(1 p) is charged. (c) The market closes, trading stops. 2. Arbitration stage. (a) Each arbiter i receives a signal xi {0, 1} and reports an outcome ˆxi {0, 1}. (b) Each arbiter i is assigned a peer arbiter j = i and paid according to the 1/prior with midpoint mechanism. (c) The outcome of the market, and the payoff of each share sold, is set to the fraction of arbiters that report ˆxi = 1. |
| Open Source Code | No | The paper does not provide any statement or link indicating that its source code is publicly available. |
| Open Datasets | No | The paper is theoretical and does not describe experiments using any specific dataset, public or otherwise, nor does it provide any access information or citations for data. |
| Dataset Splits | No | The paper does not describe any experimental setup involving datasets, so there is no mention of training, validation, or test splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments that would require specific software with version numbers, therefore no software dependencies are mentioned. |
| Experiment Setup | No | The paper is theoretical and focuses on mechanism design and analysis. It does not describe an experimental setup with hyperparameters or training configurations. |