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Updated: Jun 14, 2025

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Joint-outcome prediction markets for climate risks.

Mark S Roulston^1,2, Kim Kaivanto^2,3

¹Economics and Policy Institute, Land, Environment, University of Exeter, Exeter, United Kingdom.

|August 30, 2024

Summary

This summary is machine-generated.

Prediction markets can forecast climate risks by simultaneously predicting greenhouse gas emissions and temperature. These markets aggregate expert judgment for more credible climate change policy and planning.

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Published on: July 24, 2016

Area of Science:

Climate Science
Environmental Economics
Computational Social Science

Background:

Climate change prediction necessitates interdisciplinary expertise and must account for policy-driven feedback loops (circularity).
Long-range climate forecasting faces information asymmetry, hindering user trust in provider track records.
Existing forecasting methods struggle with aggregating diverse expert knowledge and addressing prediction circularity.

Purpose of the Study:

To explore the use of prediction markets with joint-outcome spaces to address challenges in climate change forecasting.
To test the viability of granular prediction markets for aggregating expert judgment on climate-related variables.

Main Methods:

Designed prediction markets with joint-outcome spaces for simultaneous forecasting of greenhouse gas (GHG) concentrations and temperature.
Implemented granular markets for monthly UK rainfall and temperature to test aggregation capabilities.
Utilized expert judgments within a market structure to simulate real-world forecasting scenarios.

Main Results:

Demonstrated that prediction markets can effectively aggregate the judgments of experts with relevant climate science and policy expertise.
Validated the feasibility of using highly granular, joint-outcome prediction markets for complex forecasting tasks.
Showcased the potential for these markets to overcome information asymmetry and circularity issues in climate prediction.

Conclusions:

Prediction markets with joint-outcome spaces offer a promising mechanism for producing credible climate change forecasts.
These markets can support evidence-based policy-making, risk assessment, and strategic planning for climate-related challenges.
Future applications could involve longer-term markets for global climate risks and policy-relevant scenarios.