bitcoin
Bitcoin (BTC) $ 54,670.65 5.61%
ethereum
Ethereum (ETH) $ 3,181.17 2.42%
tether
Tether (USDT) $ 1.00 0.02%
omni
Omni (OMNI) $ 1.51 38.89%
bnb
BNB (BNB) $ 402.65 3.75%
usd-coin
USDC (USDC) $ 1.00 0.02%
xrp
XRP (XRP) $ 0.551282 1.64%
cardano
Cardano (ADA) $ 0.622116 5.38%
dogecoin
Dogecoin (DOGE) $ 0.089529 4.07%
staked-ether
Lido Staked Ether (STETH) $ 3,177.17 2.49%
matic-network
Polygon (MATIC) $ 1.06 7.10%
solana
Solana (SOL) $ 110.51 6.90%
polkadot
Polkadot (DOT) $ 8.12 2.68%
litecoin
Litecoin (LTC) $ 72.24 2.94%
avalanche-2
Avalanche (AVAX) $ 39.44 5.76%
shiba-inu
Shiba Inu (SHIB) $ 0.00001 4.34%
binance-usd
BUSD (BUSD) $ 1.00 0.06%
dai
Dai (DAI) $ 0.999704 0.18%
uniswap
Uniswap (UNI) $ 10.62 3.50%
wrapped-bitcoin
Wrapped Bitcoin (WBTC) $ 54,609.63 5.57%
chainlink
Chainlink (LINK) $ 19.11 2.14%
cosmos
Cosmos Hub (ATOM) $ 11.15 7.48%
the-open-network
Toncoin (TON) $ 2.14 2.78%
leo-token
LEO Token (LEO) $ 4.34 2.21%
okb
OKB (OKB) $ 51.25 1.92%
ethereum-classic
Ethereum Classic (ETC) $ 28.01 3.07%
monero
Monero (XMR) $ 134.35 3.89%
stellar
Stellar (XLM) $ 0.117841 1.00%
filecoin
Filecoin (FIL) $ 8.14 0.72%
bitcoin-cash
Bitcoin Cash (BCH) $ 276.03 3.09%
aptos
Aptos (APT) $ 10.12 5.04%
lido-dao
Lido DAO (LDO) $ 3.62 6.98%
arbitrum
Arbitrum (ARB) $ 1.93 1.35%
hedera-hashgraph
Hedera (HBAR) $ 0.111514 2.35%
near
NEAR Protocol (NEAR) $ 4.12 12.19%
true-usd
TrueUSD (TUSD) $ 0.977812 0.34%
vechain
VeChain (VET) $ 0.05063 11.19%
internet-computer
Internet Computer (ICP) $ 12.95 3.41%
crypto-com-chain
Cronos (CRO) $ 0.098079 3.82%
quant-network
Quant (QNT) $ 109.05 2.15%
apecoin
ApeCoin (APE) $ 1.99 7.87%
algorand
Algorand (ALGO) $ 0.211112 2.11%
the-graph
The Graph (GRT) $ 0.293288 0.38%
fantom
Fantom (FTM) $ 0.436746 4.50%
eos
EOS (EOS) $ 0.81501 2.12%
the-sandbox
The Sandbox (SAND) $ 0.541704 4.58%
decentraland
Decentraland (MANA) $ 0.538147 4.77%
aave
Aave (AAVE) $ 102.88 1.98%
blockstack
Stacks (STX) $ 3.00 16.21%
theta-token
Theta Network (THETA) $ 1.79 28.39%
elrond-erd-2
MultiversX (EGLD) $ 60.94 5.58%
tezos
Tezos (XTZ) $ 1.15 2.43%
flow
Flow (FLOW) $ 1.07 4.97%
rocket-pool
Rocket Pool (RPL) $ 31.27 2.32%
axie-infinity
Axie Infinity (AXS) $ 8.63 6.40%
frax
Frax (FRAX) $ 0.998863 0.19%
immutable-x
Immutable (IMX) $ 3.34 3.91%
paxos-standard
Pax Dollar (USDP) $ 0.999559 0.06%
neo
NEO (NEO) $ 13.18 3.09%
radix
Radix (XRD) $ 0.048189 1.69%
Can Machine Learning Predict A Revolution? | Philip Schrodt | Wondros Podcast Ep 188

Originally aired on Jan 25, 2023

Jesse and Priscilla discuss using machine learning to forecast conflict and social uprisings with Philip Schrodt, mathematician and senior research political scientist of the statistical consulting firm Parus Analytical Systems.  Philip explains the inherent complexity of political systems and the challenges of predicting significant events.

Can using machine learning help to predict political unrest and revolution?

  • Philip Schrodt became interested in predicting significant events during his joint degree in mathematics and political science.
  • The study of political systems is complex and requires input from various fields.
  • Machine learning can help identify patterns and possible triggers for political unrest.
  • Predicting significant events is challenging due to the dynamic nature of politics and the emergence of unexpected factors.
  • The application of machine learning to predicting significant events is still in the early stages and requires further development.
  • Policymakers can use machine learning techniques to better anticipate and prepare for potential crises.
  • Machine learning can help inform decision-making and guide resource allocation to areas most at risk of political unrest.