Prediction Markets and Beliefs about Climate: Results from Agent-Based SimulationsJonathan M. Gilligan,1 John J. Nay,2 Martin Van der Linden3

1. Earth & Environmental Sciences, Vanderbilt U., Nashville TN, USA; 2. School of Engineering, Vanderbilt U., Nashville TN, USA; 3. Economics, Vanderbilt U., Nashville TN, USA

PA23B-2191

Further Reading: Contact: https://my.vanderbilt.edu/jonathangilligan

jonathan.gilligan@vanderbilt.edu

Premise• Many people unconvinced by scientific evidence of climate

change.• Cultural cognition:

• Ideological opposition to trusting climate scientists.• Trust in free markets

• J. Annan and others: • Bets and prediction markets• “Put your money where your mouth is.”• Good for testing sincerity of doubters.

• But can prediction markets change minds of sincere doubters?MP Vandenbergh, KT Raimi, & JM Gilligan. UCLA Law Rev. 61, 1962 (2014).

Model• Global Temperature:

• Start with historical temperature record.• Project future climates under two alternate theories:

• Temperature proportional to log(CO2) or Total Solar Irradiance (conventional science vs. popular alternative among doubters)

• Add AR(1) noise (best ARMA noise model for historical record).• Traders:

• Traders believe temperature depends on CO2 or TSI• Parameterize trader ideology (resistance to changing mind)

and tolerance for investment risk.• Traders compare their profits to others in their social network

• Network characterized by # edges (connections) and segmentation (are traders with different initial beliefs connected?)

• Prediction Market:• Continuous-Double Auction (typical of large stock exchange)• Climate futures:

• Bet on temperature six years in future.• During six-year period, traders buy and sell futures.• Every year: Bayesian updating of traders personal predictions for

future temperature based on current year’s temperature.• At end of six-year period, winners collect money• Traders revise beliefs about climate models,

based on ideology and beliefs of top earners in their social network.

Trader Network

Results:• Traders beliefs converge

toward true model• Convergence is slow• Depends on

network topology and trader ideology

Discussion:• Proof of concept, not a rigorous analysis

of prediction markets• Prediction markets might help

persuade doubters who do not trust scientists.

• However, persuasion might be too slow for stabilization targets.

• Echo-chamber (segmented social networks).

• This simulation uses very simple belief model.

• Next steps:• True Bayesian belief model• Multiple influences (neighbors and share

prices)• Compare psychological and economic

theories of belief.• Compare different types of securities.

Historical Simulation:• Use actual temperatures 1880-2015.• Betting from 1931–2014.• As warming signal begins to show

up in 1980s, traders begin to converge toward log(CO2) model.

CO2: IAMC RCP database (8.5 shown); TSI: VM Velasco Herrera, B Mendoza,

& G Velasco Herrera, New Astronomy 34, 221 (2015)